Abstract .uk



Autologous CAR T-cell therapies supply chain: Challenges and Opportunities?Maria M. Papathanasioua, Christos Stamatisb,c, Matthew Lakelind, Suzanne Faridb,c, Nigel Titchener-Hookerb,c, Nilay ShahaaDept. of Chemical Engineering, Centre for Process Systems Engineering (CPSE), Imperial College London SW7 2AZ, Lodnon, U.K b Dept. of Biochemical Engineering, University College London, London WC1E 7JE, UKc The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, University College London, Torrington Place, London WC1E 7JE, UKd TrakCel Limited, 10/11 Raleigh Walk, Cardiff, CF10 4LN UKAbstractChimeric Antigen Receptor (CAR) T cells are considered a potentially disruptive cancer therapy, showing highly promising results. Their recent success and regulatory approval (both in the USA and Europe) are likely to generate a rapidly increasing demand and a need for the design of robust and scalable manufacturing and distribution models that will ensure timely and cost-effective delivery of the therapy to the patient. However, there are challenging tasks as these therapies are accompanied by a series of constraints and particularities that need to be taken into consideration in the decision-making process. Here, we present an overview of the current state-of-the-art in the CAR T cell market and present novel concepts that can debottleneck key elements of the current supply chain model and, we believe, help this technology achieve its long-term potential.IntroductionChimeric Antigen Receptors (CARs) are recombinant receptors for antigens that can make T lymphocytes tumour-specific. Through genetic modification, T cells are engineered to express the CAR receptor that redirects their specificity and function, enabling them to recognize and destroy cancer cells ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.14694/EdBook_AM.2015.35.e360","ISSN":"1548-8748","PMID":"25993197","abstract":"Chimeric antigen receptor (CAR) therapy is an emerging immunotherapy that shows great promise for cancer, in particular acute lymphoblastic leukemia (ALL). CARs are recombinant receptors for antigen, which, in a single molecule, redirect the specificity and function of T lymphocytes. Following their genetic transfer to patient T cells, the latter acquire the ability to recognize leukemia cells and destroy them. Several years ago, we identified CD19 as an attractive target for CAR therapy for most B cell malignancies, including ALL. We and others have reported remarkable clinical outcomes in adults and children with ALL, achieving a high complete remission rate irrespective of age, prior treatments, or other prognostic markers. Severe cytokine release may develop in patients with high tumor burdens. Several interventions are available to curb the cytokine release syndrome when it occurs. Based on the impressive results obtained with CD19 CAR therapy for ALL, it is realistic to expect that CD19 CARs will become part of the armamentarium for B cell-ALL and other B cell malignancies.","author":[{"dropping-particle":"","family":"Sadelain","given":"Michel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brentjens","given":"Renier","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rivière","given":"Isabelle","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Park","given":"Jae","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American Society of Clinical Oncology Educational Book","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"page":"e360-e363","title":"CD19 CAR Therapy for Acute Lymphoblastic Leukemia","type":"article-journal","volume":"35"},"uris":[""]}],"mendeley":{"formattedCitation":"(Sadelain <i>et al.</i>, 2015)","plainTextFormattedCitation":"(Sadelain et al., 2015)","previouslyFormattedCitation":"(Sadelain <i>et al.</i>, 2015)"},"properties":{"noteIndex":0},"schema":""}(Sadelain et al., 2015). This individualized, emerging immunotherapy has shown promising results particularly in the treatment of B-cell lymphoma ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1056/NEJMoa1707447","ISBN":"8812288138","ISSN":"0028-4793","PMID":"29226797","abstract":"BackgroundIn a phase 1 trial, axicabtagene ciloleucel (axi-cel), an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy, showed efficacy in patients with refractory large B-cell lymphoma after the failure of conventional therapy. MethodsIn this multicenter, phase 2 trial, we enrolled 111 patients with diffuse large B-cell lymphoma, primary mediastinal B-cell lymphoma, or transformed follicular lymphoma who had refractory disease despite undergoing recommended prior therapy. Patients received a target dose of 2×106 anti-CD19 CAR T cells per kilogram of body weight after receiving a conditioning regimen of low-dose cyclophosphamide and fludarabine. The primary end point was the rate of objective response (calculated as the combined rates of complete response and partial response). Secondary end points included overall survival, safety, and biomarker assessments. ResultsAmong the 111 patients who were enrolled, axi-cel was successfully manufactured for 110 (99%) and administered to 101 (91%)....","author":[{"dropping-particle":"","family":"Neelapu","given":"Sattva S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Locke","given":"Frederick L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bartlett","given":"Nancy L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lekakis","given":"Lazaros J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Miklos","given":"David B.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jacobson","given":"Caron A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Braunschweig","given":"Ira","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oluwole","given":"Olalekan O.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Siddiqi","given":"Tanya","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lin","given":"Yi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Timmerman","given":"John M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stiff","given":"Patrick J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Friedberg","given":"Jonathan W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Flinn","given":"Ian W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goy","given":"Andre","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hill","given":"Brian T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Smith","given":"Mitchell R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Deol","given":"Abhinav","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Farooq","given":"Umar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McSweeney","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Munoz","given":"Javier","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Avivi","given":"Irit","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Castro","given":"Januario E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Westin","given":"Jason R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chavez","given":"Julio C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghobadi","given":"Armin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V.","family":"Komanduri","given":"Krishna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Levy","given":"Ronald","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jacobsen","given":"Eric D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Witzig","given":"Thomas E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reagan","given":"Patrick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bot","given":"Adrian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rossi","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Navale","given":"Lynn","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jiang","given":"Yizhou","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aycock","given":"Jeff","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elias","given":"Meg","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chang","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wiezorek","given":"Jeff","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Go","given":"William Y.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"New England Journal of Medicine","id":"ITEM-1","issue":"26","issued":{"date-parts":[["2017","12","28"]]},"page":"NEJMoa1707447","publisher":"Massachusetts Medical Society","title":"Axicabtagene Ciloleucel CAR T-Cell Therapy in Refractory Large B-Cell Lymphoma","type":"article-journal","volume":"377"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1056/NEJMoa1709866","ISBN":"0028-4793","ISSN":"0028-4793","PMID":"29385370","abstract":"BACKGROUND In a single-center phase 1-2a study, the anti-CD19 chimeric antigen receptor (CAR) T-cell therapy tisagenlecleucel produced high rates of complete remission and was associated with serious but mainly reversible toxic effects in children and young adults with relapsed or refractory B-cell acute lymphoblastic leukemia (ALL). METHODS We conducted a phase 2, single-cohort, 25-center, global study of tisagenlecleucel in pediatric and young adult patients with CD19+ relapsed or refractory B-cell ALL. The primary end point was the overall remission rate (the rate of complete remission or complete remission with incomplete hematologic recovery) within 3 months. RESULTS For this planned analysis, 75 patients received an infusion of tisagenlecleucel and could be evaluated for efficacy. The overall remission rate within 3 months was 81%, with all patients who had a response to treatment found to be negative for minimal residual disease, as assessed by means of flow cytometry. The rates of event-free survival and overall survival were 73% (95% confidence interval [CI], 60 to 82) and 90% (95% CI, 81 to 95), respectively, at 6 months and 50% (95% CI, 35 to 64) and 76% (95% CI, 63 to 86) at 12 months. The median duration of remission was not reached. Persistence of tisagenlecleucel in the blood was observed for as long as 20 months. Grade 3 or 4 adverse events that were suspected to be related to tisagenlecleucel occurred in 73% of patients. The cytokine release syndrome occurred in 77% of patients, 48% of whom received tocilizumab. Neurologic events occurred in 40% of patients and were managed with supportive care, and no cerebral edema was reported. CONCLUSIONS In this global study of CAR T-cell therapy, a single infusion of tisagenlecleucel provided durable remission with long-term persistence in pediatric and young adult patients with relapsed or refractory B-cell ALL, with transient high-grade toxic effects. (Funded by Novartis Pharmaceuticals; number, NCT02435849 .).","author":[{"dropping-particle":"","family":"Maude","given":"Shannon L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Laetsch","given":"Theodore W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buechner","given":"Jochen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rives","given":"Susana","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boyer","given":"Michael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bittencourt","given":"Henrique","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bader","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Verneris","given":"Michael R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stefanski","given":"Heather E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Myers","given":"Gary D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Qayed","given":"Muna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moerloose","given":"Barbara","non-dropping-particle":"De","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hiramatsu","given":"Hidefumi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schlis","given":"Krysta","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Davis","given":"Kara L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martin","given":"Paul L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nemecek","given":"Eneida R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yanik","given":"Gregory A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Peters","given":"Christina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Baruchel","given":"Andre","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boissel","given":"Nicolas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mechinaud","given":"Francoise","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Balduzzi","given":"Adriana","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Krueger","given":"Joerg","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"June","given":"Carl H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Levine","given":"Bruce L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wood","given":"Patricia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Taran","given":"Tetiana","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Leung","given":"Mimi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mueller","given":"Karen T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Yiyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sen","given":"Kapildeb","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lebwohl","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pulsipher","given":"Michael A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grupp","given":"Stephan A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"New England Journal of Medicine","id":"ITEM-2","issue":"5","issued":{"date-parts":[["2018","2"]]},"page":"439-448","publisher":"Massachusetts Medical Society","title":"Tisagenlecleucel in Children and Young Adults with B-Cell Lymphoblastic Leukemia","type":"article-journal","volume":"378"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1038/nrclinonc.2016.36","ISSN":"1759-4774","abstract":"Clinical trials of CAR-T-cell therapy for patients with B-cell malignancies have yielded impressive results. Ongoing clinical trials are now testing CAR-T-cell therapies with new designs for the treatment of other haematological and solid malignancies. The authors of this Review present an overview of the approaches that are currently being tested in registered clinical trials, and discuss strategies that can increase the antitumour efficacy and safety of CAR-T-cell therapy.","author":[{"dropping-particle":"","family":"Jackson","given":"Hollie J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rafiq","given":"Sarwish","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brentjens","given":"Renier J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature Reviews Clinical Oncology","id":"ITEM-3","issue":"6","issued":{"date-parts":[["2016","6","22"]]},"page":"370-383","publisher":"Nature Publishing Group","title":"Driving CAR T-cells forward","type":"article-journal","volume":"13"},"uris":[""]}],"mendeley":{"formattedCitation":"(Jackson, Rafiq and Brentjens, 2016; Neelapu <i>et al.</i>, 2017; Maude <i>et al.</i>, 2018)","plainTextFormattedCitation":"(Jackson, Rafiq and Brentjens, 2016; Neelapu et al., 2017; Maude et al., 2018)","previouslyFormattedCitation":"(Jackson, Rafiq and Brentjens, 2016; Neelapu <i>et al.</i>, 2017; Maude <i>et al.</i>, 2018)"},"properties":{"noteIndex":0},"schema":""}(Jackson, Rafiq and Brentjens, 2016; Neelapu et al., 2017; Maude et al., 2018) and has encouraged further clinical research. In August 2017, the U.S. Food and Drug Administration (FDA) gave an historic approval of the first autologous, cell-based cancer therapy that has. pPotentially this changed the future of cancer therapies. Novartis’ Kymriah, is an autologous CAR T-cell therapy for B-cell acute lymphoblastic leukaemia (ALL) and is the first such therapy to other innovative cancer treatments. Following that, Kite’s Yescarta was approved by the U.S. FDA in October 2017. The therapy is indicated for the treatment of adult patients with relapsed or refractory large B-cell lymphoma. Both of the CAR T cell therapies are showing promising results with remission rates significantly higher compared to chemotherapy. Recently, both therapies received approval from the European Medicines Agency (EMA) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2018","9","10"]]},"author":[{"dropping-particle":"","family":"EMA","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2018"]]},"title":"First two CAR-T cell medicines recommended for approval in the European Union","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(EMA, 2018)","plainTextFormattedCitation":"(EMA, 2018)","previouslyFormattedCitation":"(EMA, 2018)"},"properties":{"noteIndex":0},"schema":""}(EMA, 2018). The therapies are available through restricted programs (Risk Evaluation and Mitigation Strategies (REMS) for the USA) and PRIority MEdicines (PRIME) for Europe) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2018","6","13"]]},"author":[{"dropping-particle":"","family":"FDA","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2015"]]},"title":"Kymriah, Approved Risk Evaluation and Mitigation Strategies (REMS)","type":"webpage"},"uris":[""]},{"id":"ITEM-2","itemData":{"URL":"","accessed":{"date-parts":[["2018","6","13"]]},"author":[{"dropping-particle":"","family":"FDA","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-2","issued":{"date-parts":[["2015"]]},"title":"Yescarta, Approved Risk Evaluation and Mitigation Strategies (REMS)","type":"webpage"},"uris":[""]},{"id":"ITEM-3","itemData":{"URL":"","accessed":{"date-parts":[["2018","9","10"]]},"author":[{"dropping-particle":"","family":"EMA","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-3","issued":{"date-parts":[["2018"]]},"title":"First two CAR-T cell medicines recommended for approval in the European Union","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(FDA, 2015a, 2015b; EMA, 2018)","plainTextFormattedCitation":"(FDA, 2015a, 2015b; EMA, 2018)","previouslyFormattedCitation":"(FDA, 2015a, 2015b; EMA, 2018)"},"properties":{"noteIndex":0},"schema":""}(FDA, 2015a, 2015b; EMA, 2018). Given their high manufacturing, distribution and administration costs the two marketed therapies are offered at a relatively high list price ($475,000 for Kymriah and $373,000 for Yescarta). The promising results of the two marketed therapies have encouraged further research in the field thatresulting resulted in Currently there are numerous clinical trials ( REF _Ref24042238 \h Figure 1) on, both autologous and allogeneic products. Currently, China and the United States act as the main hubs, hosting almost 80% of the 317 global CAR-T clinical trials. Figure SEQ Figure \* ARABIC 1 Map of clinical trials1 currently on Chimeric Antigen Receptor T cell therapies (Source: ). 1 The results were generated using “CHIMERIC OR CAR OR CAR T OR B-cell OR T-cell OR NHL OR FL OR HL OR HODGKIN OR SOLID AND CAR T-CELL” as search terms.Despite the fact that haematological malignancies represent only a small fraction of human cancer ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","abstract":"Cancer incidence and mortality statistics for cancers diagnosed worldwide in 2008","accessed":{"date-parts":[["2018","6","12"]]},"author":[{"dropping-particle":"","family":"Uk","given":"Cancer Research","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cancer research UK","id":"ITEM-1","issued":{"date-parts":[["2014"]]},"page":"2012-2015","title":"Worldwide cancer statistics","type":"webpage","volume":"2012"},"uris":[""]}],"mendeley":{"formattedCitation":"(Uk, 2014)","plainTextFormattedCitation":"(Uk, 2014)","previouslyFormattedCitation":"(Uk, 2014)"},"properties":{"noteIndex":0},"schema":""}(UK, 2014), they are inat the forefront the spotlight of clinical research for the advancement of cancer treatment (currently 233 listed clinical trials, 7 of which are on related equipment/procedure). On the other hand, the complexity in the characterization of solid tumours (anatomic location, histology, immunohistochemical strains) and the lack of a single, direct CAR target, pose additional challenges related to on-target off-tumour toxicity ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.blre.2015.10.003","ISBN":"1532-1681 (Electronic)\\r0268-960X (Linking)","ISSN":"15321681","PMID":"26574053","abstract":"Chimeric antigen receptor (CAR) T cell therapy of cancer is generating enormous enthusiasm. Twenty-five years after the concept was first proposed, major advances in molecular biology, virology, and good manufacturing practices (GMP)-grade cell production have transformed antibody-T cell chimeras from a scientific curiosity to a fact of life for academic cellular immunotherapy researchers and, increasingly, for patients. In this review, we explain the preclinical concept, outline how it has been translated to the clinic, and draw lessons from the first years of CAR T cell therapy for the practicing clinician.","author":[{"dropping-particle":"","family":"Gill","given":"Saar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V.","family":"Maus","given":"Marcela","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Porter","given":"David L.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Blood Reviews","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2016"]]},"page":"157-167","publisher":"Elsevier B.V.","title":"Chimeric antigen receptor T cell therapy: 25 years in the making","type":"article-journal","volume":"30"},"uris":[""]}],"mendeley":{"formattedCitation":"(Gill, Maus and Porter, 2016)","plainTextFormattedCitation":"(Gill, Maus and Porter, 2016)","previouslyFormattedCitation":"(Gill, Maus and Porter, 2016)"},"properties":{"noteIndex":0},"schema":""}(Gill, Maus and Porter, 2016). Nevertheless, the documented CAR trials for solid tumours are currently 94, while clinical interest in these therapies is gradually growing. Current research is also focusing on advances in the use of different target antigens, aiming to develop CAR T cell therapies for other malignancy types ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/nrclinonc.2016.36","ISSN":"1759-4774","abstract":"Clinical trials of CAR-T-cell therapy for patients with B-cell malignancies have yielded impressive results. Ongoing clinical trials are now testing CAR-T-cell therapies with new designs for the treatment of other haematological and solid malignancies. The authors of this Review present an overview of the approaches that are currently being tested in registered clinical trials, and discuss strategies that can increase the antitumour efficacy and safety of CAR-T-cell therapy.","author":[{"dropping-particle":"","family":"Jackson","given":"Hollie J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rafiq","given":"Sarwish","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brentjens","given":"Renier J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature Reviews Clinical Oncology","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2016","6","22"]]},"page":"370-383","publisher":"Nature Publishing Group","title":"Driving CAR T-cells forward","type":"article-journal","volume":"13"},"uris":[""]}],"mendeley":{"formattedCitation":"(Jackson, Rafiq and Brentjens, 2016)","plainTextFormattedCitation":"(Jackson, Rafiq and Brentjens, 2016)","previouslyFormattedCitation":"(Jackson, Rafiq and Brentjens, 2016)"},"properties":{"noteIndex":0},"schema":""}(Jackson, Rafiq and Brentjens, 2016). REF _Ref523297376 \h \* MERGEFORMAT Table 1 summarizes the main target antigens currently in study, and the targeted cancer type.Table SEQ Table \* ARABIC 1 Antigen types currently under study and the targeted cancer type (list based on results on clinical trials currently in place).Cancer typeAntigenB cell malignanciesCD19, CD20, CD22, CD23, CD30, ROR1, kappa light chain, PD-1AMLCD28, CD128, CD33, CD44, CD44v6, NKG2DHodgkin LymphomaCD30T cell malignanciesCD5, CD30MyelomaCD138, CS-1, CD38, NKG2D, CD44v6, BCMA, CD19Solid Tumoursanti-HER2/neu, EGFRvIII, GD-2, CEA, FAP, Glypican 3, Mesothelin, IL13Ra2Despite their initial success, CAR T cell therapies face a series of challenges that need to be tackled to facilitate and ensure their smooth and stable establishment in the drug market. Such challenges are associated with various steps throughout the CAR T cell therapies manufacturing, supply and licensing process. The manufacturing process of CAR T cell therapies is highly complicated, as it comprises a large number of steps which are challenging to perform and coordinate. In addition, processing steps are currently operated in batch mode – often in different locations – thus increasing the complexity. Moreover, the supply chain model currently followed by the CAR T cell industry is able to serve a finite number of patients that does not go beyond the order of hundreds per region/country annually. Therefore, as we move forward with the likely establishment of CAR T cell therapies as key therapeutic options in cancer treatment, the current models will prove to be challenging to scale up (autologous processes cannot be scaled up volumetrically) and thus require significant improvements. Furthermore, challenges arise from the regulation and reimbursement procedures associated with CAR T cell therapies, as the latter are characterised by a significantly high cost and a complex chain of custody.The aforementioned challenges will become more profound as patient numbers increase. Today, most of the patients are treated with CAR T cell therapiesTaking the UK as an example, currently a maximum of 1000 patients are receiving CAR T cell therapies across all active trials, available at specialised centres that do not exceed the order of 10. However, based on reports by the Haematological Malignancy Research Network (HMRN) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2018","7","20"]]},"author":[{"dropping-particle":"","family":"HMRN (Haematological Malignancy Research Network)","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Epidemiology & Cancer Statistics Group Department of Health Sciences Area 3 Seebohm Rowntree Building YORK YO10 5DD","id":"ITEM-1","issued":{"date-parts":[["2018"]]},"title":"Statistics","type":"webpage"},"suppress-author":1,"uris":[""]}],"mendeley":{"formattedCitation":"(2018)","plainTextFormattedCitation":"(2018)","previouslyFormattedCitation":"(2018)"},"properties":{"noteIndex":0},"schema":""}(2018) and forecasts on the population that we performed in this work (Appendix A), these figures are expected to increase, as the population is growing, and CAR T cell therapies are likely to become available for other cancer types, potentially reaching 40,000 people by year 2031 (Appendix A). Furthermore, in an effort to maximise the success of the therapy with respect to the administration, the UK government awarded ?21M through the Industrial Strategy Challenge Fund, with the creation of a UK-wide network Advanced Therapies Treatment Centres (ATTCs). ATTCs will be responsible for the supply, maintenance and delivery of those medicines in the NHS (Figure 2). Each of the ATTCs are themselves formed of several organisations (hospitals, research centres, industrial manufacturers), serving an extended geographical region ( REF _Ref24046276 \h Figure 2). Each ATTC is depicted using bubble size to illustrate the different number of collaborating hospitals in each ATTC. Despite the large number of total collaborating hospitals (approximately 29), there are areas that are underrepresented, such as London and South East England, as well as Northern Ireland. The emerging ATTCs strategy is a centralised model, where fewer, large ATTCs are established, each to serve multiple geographical locations. This may imply a necessary transition in the future, in order to create a scalable, decentralised delivery network, ensuring adequate supply for the rapidly increasing patient population.Figure SEQ Figure \* ARABIC 2 Visual representation of the Advanced Therapies Treatment Centres in the UK. Bubble sizes refer to the different number of hospitals in each region.In this paper we identify and discuss challenges in CAR T cell therapy manufacturing and supply chain arising from: (a) the increasing demand, (b) the nature of the process and/or product nature and (c) the increasingly complex logistics. Autologous CAR T cells lifecycle: The current state-of-the-artIn order to understand fully the complexity of the supply chain and associated logistics in autologous CAR T cell therapies, it is important to map out the lifecycle of the therapy from collection to delivery (vein-to-vein). REF _Ref520216733 \h \* MERGEFORMAT Figure 5 depicts aA typical lifecycle followed in the production and delivery ofin autologous CAR T cell therapies . The cycle comprises three main steps: (a) leukapheresis (cell collection), (b) therapy manufacturing and (c) therapy administration ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/cgt.2014.78","ISSN":"0929-1903","abstract":"Towards a commercial process for the manufacture of genetically modified T cells for therapy","author":[{"dropping-particle":"","family":"Kaiser","given":"A D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Assenmacher","given":"M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schr?der","given":"B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Meyer","given":"M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Orentas","given":"R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bethke","given":"U","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dropulic","given":"B","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cancer Gene Therapy","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2015","2","23"]]},"page":"72-78","publisher":"Nature Publishing Group","title":"Towards a commercial process for the manufacture of genetically modified T cells for therapy","type":"article-journal","volume":"22"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1016/j.omtm.2016.12.006","ISBN":"2329-0501 2329-0501","ISSN":"23290501","PMID":"28344995","abstract":"Immunotherapy using chimeric antigen receptor-modified T cells has demonstrated high response rates in patients with B cell malignancies, and chimeric antigen receptor T cell therapy is now being investigated in several hematologic and solid tumor types. Chimeric antigen receptor T cells are generated by removing T cells from a patient's blood and engineering the cells to express the chimeric antigen receptor, which reprograms the T cells to target tumor cells. As chimeric antigen receptor T cell therapy moves into later-phase clinical trials and becomes an option for more patients, compliance of the chimeric antigen receptor T cell manufacturing process with global regulatory requirements becomes a topic for extensive discussion. Additionally, the challenges of taking a chimeric antigen receptor T cell manufacturing process from a single institution to a large-scale multi-site manufacturing center must be addressed. We have anticipated such concerns in our experience with the CD19 chimeric antigen receptor T cell therapy CTL019. In this review, we discuss steps involved in the cell processing of the technology, including the use of an optimal vector for consistent cell processing, along with addressing the challenges of expanding chimeric antigen receptor T cell therapy to a global patient population.","author":[{"dropping-particle":"","family":"Levine","given":"Bruce L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Miskin","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wonnacott","given":"Keith","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Keir","given":"Christopher","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Molecular Therapy - Methods and Clinical Development","id":"ITEM-2","issue":"March","issued":{"date-parts":[["2017"]]},"page":"92-101","publisher":"Elsevier Ltd.","title":"Global Manufacturing of CAR T Cell Therapy","type":"article-journal","volume":"4"},"uris":[""]}],"mendeley":{"formattedCitation":"(Kaiser <i>et al.</i>, 2015; Levine <i>et al.</i>, 2017)","plainTextFormattedCitation":"(Kaiser et al., 2015; Levine et al., 2017)","previouslyFormattedCitation":"(Kaiser <i>et al.</i>, 2015; Levine <i>et al.</i>, 2017)"},"properties":{"noteIndex":0},"schema":""}(Kaiser et al., 2015; Levine et al., 2017). The successful operation of such a supply chain model requires the orchestration of multiple components ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/pchemeng.2003.09.022","ISBN":"0098-1354","ISSN":"00981354","abstract":"Supply chain optimisation is now a major research theme in process operations and management. A great deal of research has been undertaken on facility location and design, inventory and distribution planning, capacity and production planning and detailed scheduling. Only a small proportion of this work directly addresses the issues faced in the pharmaceutical sector. On the other hand, this sector is very much ready for and in need of sophisticated supply chain optimisation techniques. At the supply chain design stage, a particular problem faced by this industry is the need to balance future capacity with anticipated demands in the face of the very significant uncertainty that arises out of clinical trials and competitor activity. Efficient capacity utilisation plans and robust infrastructure investment decisions will be important as regulatory pressures increase and margins are eroded. The ability to locate nodes of the supply chain in tax havens and optimise trading and transfer price structures results in interesting degrees of freedom in the supply chain design problem. Prior even to capacity planning comes the problem of pipeline and testing planning, where the selection of products for development and the scheduling of the development tasks requires a careful management of risk and potential rewards. At the operation stage, it is often difficult to ensure responsiveness. Most pharmaceutical products involve primary active ingredient (AI) production (often multi-stage chemical synthesis or bioprocess) and secondary (formulation) production. Both of the stages are characterised by low manufacturing velocities and are hampered by the need for quality assurance activities at several points. It is not unusual for the overall supply chain cycle time to be 300 days. In this environment, supply chain debottlenecking and decoupling strategies together with co-ordinated inventory management are crucial for quick responses to changing market trends. A good understanding of what actually drives the supply chain dynamics is also required. As often as not, erratic dynamics are introduced by business processes rather than by external demand, and may be eliminated by the re-design of internal business processes or supplier/customer relationships. This paper will consider important issues in supply chain design and operation drawn from the literature and from our collaborative research projects in this area. The main features of the problems will be reviewed as wi…","author":[{"dropping-particle":"","family":"Shah","given":"Nilay","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Computers and Chemical Engineering","id":"ITEM-1","issue":"6-7","issued":{"date-parts":[["2004","6","15"]]},"page":"929-941","publisher":"Pergamon","title":"Pharmaceutical supply chains: Key issues and strategies for optimisation","type":"paper-conference","volume":"28"},"uris":[""]}],"mendeley":{"formattedCitation":"(Shah, 2004)","plainTextFormattedCitation":"(Shah, 2004)","previouslyFormattedCitation":"(Shah, 2004)"},"properties":{"noteIndex":0},"schema":""}(Shah, 2004). In a typical supply chain model in the pharmaceutical industry there are typically established warehouses/distribution centres in place, responsible for the storage and distribution of the manufactured drug to the retailers. By contrast, in the case of CAR T cells, cells are transported directly from the clinical site to the manufacturing locations and back to the hospital, thus imposing additional constraints with respect to storage, activity coordination and sample tracking. ManufacturingCAR T cell therapy manufacturing is the lengthiest and most important step of the lifecycle. Following the collection, the sample is transferred to the manufacturing site, where it undergoes a series of processing steps, to express successfully the CAR receptor. Once the product is formulated, it is assessed through Quality Control (QC) for the identification of the Critical Quality Attributes (CQAs) to ensure drug efficacy, potency and safety ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/cgt.2015.5","ISBN":"0929-1903","ISSN":"14765500","PMID":"25675873","abstract":"Performance enhancement of the immune system can now be generated through ex vivo gene modification of T cells in order to redirect native specificity to target tumor antigens. This approach combines the specificity of antibody therapy, the expanded response of cellular therapy and the memory activity of vaccine therapy. Recent clinical trials of chimeric antigen receptor (CAR) T cells directed toward CD19 as a stand-alone therapy have shown sustained complete responses in patients with acute lymphoblastic leukemia and chronic lymphocytic leukemia. As these drug products are individually derived from a patient's own cells, a different manufacturing approach is required for this kind of personalized therapy compared with conventional drugs. Key steps in the CAR T-cell manufacturing process include the selection and activation of isolated T cells, transduction of T cells to express CARs, ex vivo expansion of modified T cells and cryopreservation in infusible media. In this review, the steps involved in isolating, genetically modifying and scaling-out the CAR T cells for use in a clinical setting are described in the context of in-process and release testing and regulatory standards.","author":[{"dropping-particle":"","family":"Levine","given":"B. L.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cancer Gene Therapy","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2015","2","13"]]},"page":"79-84","publisher":"Nature Publishing Group","title":"Performance-enhancing drugs: Design and production of redirected chimeric antigen receptor (CAR) T cells","type":"article-journal","volume":"22"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1016/j.omtm.2016.12.006","ISBN":"2329-0501 2329-0501","ISSN":"23290501","PMID":"28344995","abstract":"Immunotherapy using chimeric antigen receptor-modified T cells has demonstrated high response rates in patients with B cell malignancies, and chimeric antigen receptor T cell therapy is now being investigated in several hematologic and solid tumor types. Chimeric antigen receptor T cells are generated by removing T cells from a patient's blood and engineering the cells to express the chimeric antigen receptor, which reprograms the T cells to target tumor cells. As chimeric antigen receptor T cell therapy moves into later-phase clinical trials and becomes an option for more patients, compliance of the chimeric antigen receptor T cell manufacturing process with global regulatory requirements becomes a topic for extensive discussion. Additionally, the challenges of taking a chimeric antigen receptor T cell manufacturing process from a single institution to a large-scale multi-site manufacturing center must be addressed. We have anticipated such concerns in our experience with the CD19 chimeric antigen receptor T cell therapy CTL019. In this review, we discuss steps involved in the cell processing of the technology, including the use of an optimal vector for consistent cell processing, along with addressing the challenges of expanding chimeric antigen receptor T cell therapy to a global patient population.","author":[{"dropping-particle":"","family":"Levine","given":"Bruce L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Miskin","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wonnacott","given":"Keith","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Keir","given":"Christopher","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Molecular Therapy - Methods and Clinical Development","id":"ITEM-2","issue":"March","issued":{"date-parts":[["2017"]]},"page":"92-101","publisher":"Elsevier Ltd.","title":"Global Manufacturing of CAR T Cell Therapy","type":"article-journal","volume":"4"},"uris":[""]}],"mendeley":{"formattedCitation":"(Levine, 2015; Levine <i>et al.</i>, 2017)","plainTextFormattedCitation":"(Levine, 2015; Levine et al., 2017)","previouslyFormattedCitation":"(Levine, 2015; Levine <i>et al.</i>, 2017)"},"properties":{"noteIndex":0},"schema":""}(Levine, 2015; Levine et al., 2017). Following Qualified Person (QP) release, the product is transferred to the clinical site, where it is administered to the patient. The end-to-end process for the two leading market products are reported to have target median turnaround times of 17 and 22 days for Yescarta and Kymriah respectively ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"actual approval","author":[{"dropping-particle":"","family":"U.S. Food and Drug Administration","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2017"]]},"page":"1-16","title":"Summary Basis for Regulatory Action- ATryn","type":"article-journal"},"uris":[""]},{"id":"ITEM-2","itemData":{"URL":"","accessed":{"date-parts":[["2018","7","24"]]},"author":[{"dropping-particle":"","family":"Novartis","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-2","issued":{"date-parts":[["2018"]]},"title":"Kymriah? (tisagenlecleucel), first-in-class CAR-T therapy from Novartis, receives second FDA approval to treat appropriate r/r patients with large B-cell lymphoma","type":"webpage"},"uris":[""]},{"id":"ITEM-3","itemData":{"URL":"","accessed":{"date-parts":[["2019","3","7"]]},"author":[{"dropping-particle":"","family":"Novartis","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-3","issued":{"date-parts":[["2018"]]},"title":"KYMRIAH Treatment Process, Dosing &amp; Administration | HCP","type":"webpage"},"uris":[""]},{"id":"ITEM-4","itemData":{"URL":"","accessed":{"date-parts":[["2019","3","7"]]},"author":[{"dropping-particle":"","family":"Kite Pharma","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-4","issued":{"date-parts":[["2018"]]},"title":"First CAR T Therapy for Certain Types of Relapsed or Refractory B-Cell Lymphoma","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(U.S. Food and Drug Administration, 2017; Kite Pharma, 2018; Novartis, 2018a, 2018b)","plainTextFormattedCitation":"(U.S. Food and Drug Administration, 2017; Kite Pharma, 2018; Novartis, 2018a, 2018b)","previouslyFormattedCitation":"(U.S. Food and Drug Administration, 2017; Kite Pharma, 2018; Novartis, 2018a, 2018b)"},"properties":{"noteIndex":0},"schema":""}(U.S. Food and Drug Administration, 2017; Kite Pharma, 2018; Novartis, 2018a, 2018b). Here we give an overview of the procedures followed in: (a) the leukapheresis clinical site, (b) the manufacturing site and (c) the administration clinical site.Figure SEQ Figure \* ARABIC 46 Current CAR T-cell process/distribution steps along with key bottlenecks and challengesClinical site for collectionThe leukapheresis ( REF _Ref511058306 \h \* MERGEFORMAT Figure 5Figure 6) procedure takes place at a specialized clinical site. Patient blood is extracted and the leukocytes are separated, while the remainder of the blood is returned to the patient’s circulation ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.omtm.2016.12.006","ISBN":"2329-0501 2329-0501","ISSN":"23290501","PMID":"28344995","abstract":"Immunotherapy using chimeric antigen receptor-modified T cells has demonstrated high response rates in patients with B cell malignancies, and chimeric antigen receptor T cell therapy is now being investigated in several hematologic and solid tumor types. Chimeric antigen receptor T cells are generated by removing T cells from a patient's blood and engineering the cells to express the chimeric antigen receptor, which reprograms the T cells to target tumor cells. As chimeric antigen receptor T cell therapy moves into later-phase clinical trials and becomes an option for more patients, compliance of the chimeric antigen receptor T cell manufacturing process with global regulatory requirements becomes a topic for extensive discussion. Additionally, the challenges of taking a chimeric antigen receptor T cell manufacturing process from a single institution to a large-scale multi-site manufacturing center must be addressed. We have anticipated such concerns in our experience with the CD19 chimeric antigen receptor T cell therapy CTL019. In this review, we discuss steps involved in the cell processing of the technology, including the use of an optimal vector for consistent cell processing, along with addressing the challenges of expanding chimeric antigen receptor T cell therapy to a global patient population.","author":[{"dropping-particle":"","family":"Levine","given":"Bruce L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Miskin","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wonnacott","given":"Keith","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Keir","given":"Christopher","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Molecular Therapy - Methods and Clinical Development","id":"ITEM-1","issue":"March","issued":{"date-parts":[["2017"]]},"page":"92-101","publisher":"Elsevier Ltd.","title":"Global Manufacturing of CAR T Cell Therapy","type":"article-journal","volume":"4"},"uris":[""]}],"mendeley":{"formattedCitation":"(Levine <i>et al.</i>, 2017)","plainTextFormattedCitation":"(Levine et al., 2017)","previouslyFormattedCitation":"(Levine <i>et al.</i>, 2017)"},"properties":{"noteIndex":0},"schema":""}(Levine et al., 2017). Following that, the sample is transferred to the manufacturing site for further processing. The sample can be transferred either frozen (-80oC) or cryopreserved (-180oC). This is a choice that depends on the manufacturer and the procedure that has received regulatory authorisation. In general, cryopreservation is preferred in terms of shelf-life as it allows a more flexible transport/treatment window, compared to the fresh product that has a strict 24-hour upper storage limit. Specifically, cryo transport systems can maintain temperature and quality for 10-14 days.Manufacturing siteAt the manufacturing site, cells undergo a series of modifications (enrichment, activation, genetic modification, expansion, formulation, and cryopreservation) until the final product is ready to be shipped to the hospital for administration. Most of the manufacturing steps are based on the supply of commonly available raw materials, such as medium, cell washing accessory sets and selection reagents. One of the key steps in the manufacturing of CAR T cell therapies is that of the genetic modification ( REF _Ref511058306 \h \* MERGEFORMAT Figure 6, Point 2). This processing step is responsible for the transduction of the patient T cells with the CAR receptor. This can be performed using either viral or non-viral gene transfer systems, with the former being the current standard practice. Following the successful completion of those steps, the final product is then assessed through Quality Control/Assurance, cryopreserved and transferred to the administration site. It should be underlined that QC/QA can be performed either in the manufacturer’s facilities or outsourced to a third party.Clinical site for administrationFollowing successful release of the therapy, the product is shipped to the hospital, where it is thawed and administered to the patient ( REF _Ref511058306 \h \* MERGEFORMAT Figure 6). This step is usually coordinated with the progress of the patient’s medical condition as it requires approximately 1 week of pre-conditioning, prior to the administration of the therapy.Materials managementA complete vein-to-vein procedure requires the availability of various input materials for the successful execution of each process step. As product demand scales up, management of the input material supply chain will become more important. Special focus should be given to the step of “genetic modification”, performed using viral vectors. The latter are complex products, characterized by lengthy manufacturing procedures (approximately two weeks) and a separate supply chain model. Thus, it is imperative to estimate the demand, in order to ensure that raw material shortage will not become a bottleneck.Table SEQ Table \* ARABIC 5 Materials as they are required for the completion of each process steps in the manufacturing of CAR T-cells.Process StepRaw MaterialLeukapheresis Selection accessory setComponents for selection mediumSelection reagentsActivation & Enrichment Anti-CD3/anti-CD28 antibodiesGenetic ModificationNonviral gene modification reagents(e.g. DNA plasmids, RNA)Viral VectorsExpansionComponents for expansion medium(e.g. X-VIVO15, IL-2, other cytokines)Formulation(washing concentration)Cell washing accessory sets Components in cell washing medium(e.g. phosphate buffer saline)Components in formulation mediumCryopreservationComponents used in cryopreservation medium(e.g. dimethyl sulfoxide)Storage & TransportationSimilar to the QC/QA, depending on the business model adopted by the manufacturer, storage facilities are either owned by the manufacturer or rented from a third party. Due to the short product shelf-life (i.e. 24 hours for fresh product), long storage times are not advised. Based on the process stage and the condition (fresh or cryopreserved), samples/therapies are transferred in dry shippers in order to ensure that temperature conditions are maintained at required levels. Transportation from one site to another can be by road or air. The choice of the most suitable transportation mode is often based on: (a) location of sites, (b) time restrictions and (c) weather conditions. Given the criticality of the condition of patients treated with CAR T cells, it is imperative that the therapy turnaround time is minimised. Therefore, improvements both in storage and transportation that can decrease waiting times can maintain the efficacy of the therapeutic treatment.Autologous CAR T cells lifecycle: Risks & Challenges Given the product nature and complex supply chain model, CAR T cell therapy commercialization is currently a challenging task. Issues may arise from different aspects, such as: (1) manufacturing, (2) supply chain, (3) business models, (4) reimbursement and (5) clinical adoption. Here we focus on challenges arising from the current supply chain/business model and we present an overview of risks associated with the rest of the factors.Aiming to understand better the various design and manufacturing decisions, as well as the process and other steps followed today in CAR T cell therapy manufacturing, we formulate a Resource Task Network (RTN) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Pantelides","given":"C. C.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Second conference on foundations of computer aided operations","id":"ITEM-1","issued":{"date-parts":[["1994"]]},"page":"253-274","publisher":"New York: Cache Publications","title":"Unified frameworks for optimal process planning and scheduling","type":"paper-conference"},"uris":[""]}],"mendeley":{"formattedCitation":"(Pantelides, 1994)","plainTextFormattedCitation":"(Pantelides, 1994)","previouslyFormattedCitation":"(Pantelides, 1994)"},"properties":{"noteIndex":0},"schema":""}(Pantelides, 1994) ( REF _Ref10706378 \h \* MERGEFORMAT Figure 7). The network depicts all key processes in place in CAR T cell therapy manufacturing, along with the chosen technology/procedure, intermediate products, as well as required raw materials. The complexity of the process in terms of task coordination and material supply is evident. Moreover, key decisions need to be made with respect to the condition of the leukapheresis sample. The latter can be transferred from the clinical to the manufacturing site either cryopreserved or frozen. The two alternatives imply significant differences in the supply chain model and the required processing times. Figure SEQ Figure \* ARABIC 57 Resource Task Network (RTN) for the CAR T cell therapy supply chain.Supply chain & business modelThe supply chain model currently followed in CAR T cell therapy manufacturing faces significant challenges related to scale-up/-out, timely delivery and cost. Moreover, aspects of primary importance in the design of an efficient supply chain network are: (1) sample tracking, (2) package & shipping, (3) storage & equipment validation and (4) chain-of-custody documentation. However, for a successful supply chain model, decisions on the aforementioned aspects need to be taken considering the particular product nature. In common with every other cell-based product, CAR T cells are easy to destroy due to mishandling, leading to contamination, loss of functionality or de-differentiation. Moreover, they are sensitive to temperature and stress, thus requiring experience in handling during transportation. Such risks are not always trivial, as mitigation strategies require trained personnel to be present along the journey of the sample/therapy, which may not always possible. Moreover, when designing the supply chain model, one needs also to consider the short product shelf-life that significantly restricts waiting/storage times. Furthermore, in autologous CAR T cell therapies, the patient becomes their own donor, which automatically makes the patient part of the supply chain. Patient scheduling is therefore necessary to ensure that times between collection and manufacturing, as well as product release and administration are minimized. Lastly, in autologous therapies any lost sample and/or therapy cannot be replaced from stock, as each therapy is patient-specific and follows a one-to-one model. When designing the supply chain and business model of CAR T cells, one of the key limitations that we encounter is time. Due to their nature, those therapies are accompanied by tight shelf-life windows, imposing solutions where processing, storage and shipping times must be tightly controlled. Especially in cases where the product is transferred fresh (i.e. not frozen) from leukapheresis to manufacturing, laytime should not exceed the 24-hour window. In addition to time restrictions, REF _Ref520898609 \h \* MERGEFORMAT Table 6 illustrates five main risk factors that need to be considered during CAR T cell therapy manufacturing, release and administration. One of the most important characteristics in the supply chain model of CAR T cell therapies is sample tracking (sample and patient identification). Starting with sample identification, it is evident that each product (i.e. the sample) needs to be efficiently tracked throughout the process. The tracking required here is bi-directional as it must be ensured that the right therapy will be delivered to the right patient at the end of the product cycle.Due to their autologous nature, such products do not allow errors in tracking traceability and this fact stresses the need for cloud-based platforms and application programming interfaces. The latter will allow disparate systems and information provided by stakeholders to be connected seamlessly and shared, leading to more efficient, low-risk sample tracking. As expected, patient identification is of high importance during cell harvesting, product release and treatment. During those stages, the patient needs to be identified and linked to the sample, in order: (a) for the sample to receive the patient-specific identity that is required (cell harvesting) and (b) to ensure that the patient-specific identity will be maintained, and the right therapy will return to the right patient (product release & treatment).Table SEQ Table \* ARABIC 6 Risk factors during CAR T-cell manufacturing (adapted from Griffiths & Lakelin ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"Cell therapy professionals joined a specialist webinar by industry experts from PCI Clinical Services and TrakCel, addressing the unique complexity of an autologous therapy supply chain. Hosted by European Pharmaceutical Manufacturer magazine, t he webinar was delivered by Rachel Griffiths, Associate Director, Technical Services, PCI Clini cal Services, and Dr. Matthew Lakelin, Vice President, Scientific Affairs and Business Development, TrakCel . Here, we present the white paper from that webi nar event.","author":[{"dropping-particle":"","family":"Griffiths","given":"Rachel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lakelin","given":"Matthew","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2017"]]},"page":"9","title":"Successfully managing the unique demands of cell therapy supply chains","type":"article"},"suppress-author":1,"uris":[""]}],"mendeley":{"formattedCitation":"(2017)","plainTextFormattedCitation":"(2017)","previouslyFormattedCitation":"(2017)"},"properties":{"noteIndex":0},"schema":""}(2017)).Harvesting Starting MaterialStarting Material LogisticsManufacturingProduct ReleaseTherapy LogisticsTreatmentPatient Identification HighHighLowHighLowHighSample Identification HighHighLowHighHighHighTemperature ExcursionsLowHighMediumLowHighMediumTime ExcursionsMediumHighMediumLowMediumMediumResource AllocationsHighHighHighMediumMediumHighMoreover, packaging & shipping as well as storage & equipment validation are identified as key risk factors associated with product quality and timely delivery. As mentioned above, the product of interest is of a sensitive nature, thus requiring special handling. In order to minimize risks of product loss, it is of the utmost importance to ensure that the desired conditions are met and maintained during storage and transportation. REF _Ref520898609 \h \* MERGEFORMAT Table 6 lists both temperature and time excursions as two of the most important risk factors that need to be considered during the design of novel supply chain solutions. Evidently, there is pre-eminent need for the design of shipping solutions for: (a) temperature maintenance and (b) optimal duration that can de-risk environmental and handling extremes. Similarly, the equipment used for product storage needs to be validated in order to ensure that both the desired conditions, as well as product integrity are maintained. Lastly, chain-of-custody documentation is currently challenging to address as it is required to record: (a) location, (b) security & (c) temperature, while involving various facilities, people and organizations. Lately we have seen the emergence of digital tools and software platforms (“cell orchestration platforms”) from several companies (e.g. TrakCel) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.18609/cgti.2017.081","ISSN":"20597800","author":[{"dropping-particle":"","family":"Lamb","given":"Martin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Margolin","given":"Robert E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vitale","given":"Joseph","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cell and Gene Therapy Insights","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2017","12","15"]]},"page":"815-833","title":"Personalized Supply Chains for Cell Therapies","type":"article-journal","volume":"3"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.18609/cgti.2017.067","ISSN":"20597800","author":[{"dropping-particle":"","family":"Herbert","given":"Sam","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cell and Gene Therapy Insights","id":"ITEM-2","issue":"8","issued":{"date-parts":[["2017","10","27"]]},"page":"655-662","title":"Understanding the Critical Impact of Logistics on Scale-Up &amp; Commercialization","type":"article-journal","volume":"3"},"uris":[""]}],"mendeley":{"formattedCitation":"(Herbert, 2017; Lamb, Margolin and Vitale, 2017)","plainTextFormattedCitation":"(Herbert, 2017; Lamb, Margolin and Vitale, 2017)","previouslyFormattedCitation":"(Herbert, 2017; Lamb, Margolin and Vitale, 2017)"},"properties":{"noteIndex":0},"schema":""}(Herbert, 2017; Lamb, Margolin and Vitale, 2017), designed to synchronise the various activities involved. Moreover, such tools assist with sample tracking, while keeping the patient identity and data private. Nevertheless, digitalization is often perceived as a threat that will lead to automated solutions and consequently loss of jobs. However, automation is likely to replace repetitive, error-prone tasks in human activities, allowing the operators to have more time to be creative, re-design and optimize procedures and of course be in charge of the automated operating system ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.18609/cgti.2018.050","author":[{"dropping-particle":"","family":"Papathanasiou","given":"Maria","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cell and Gene Therapy Insights","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2018","7","9"]]},"page":"495-500","title":"Advances in Enabling Smart Technologies across the Cell Therapy Supply Chain","type":"article-journal","volume":"4"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.18609/cgti.2016.027","ISSN":"20597800","abstract":"Industry 4.0 foresees a digital transformation of manufacturing resulting in smart factories and supply chains. At the heart of the concept lies the vision of interconnected materials, goods and machines, where goods find their way through the factory and the supply chain to the customer in a self-organized manner. Industry 4.0 is gaining traction in high value manufacturing sectors. This expert insight article explores what this technology driven vision has to offer the biopharmaceutical industry, and in particular cell and gene therapies.","author":[{"dropping-particle":"","family":"Branke","given":"Juergen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Farid","given":"Suzanne S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shah","given":"Nilay","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cell and Gene Therapy Insights","id":"ITEM-2","issue":"2","issued":{"date-parts":[["2016"]]},"page":"263-270","title":"Industry 4.0: a vision for personalized medicine supply chains?","type":"article-journal","volume":"2"},"uris":[""]}],"mendeley":{"formattedCitation":"(Branke, Farid and Shah, 2016; Papathanasiou, 2018)","plainTextFormattedCitation":"(Branke, Farid and Shah, 2016; Papathanasiou, 2018)","previouslyFormattedCitation":"(Branke, Farid and Shah, 2016; Papathanasiou, 2018)"},"properties":{"noteIndex":0},"schema":""}(Branke, Farid and Shah, 2016; Papathanasiou, 2018).Other associated risksAs mentioned earlier, challenges also arise from: (1) manufacturing, (2) reimbursement and (3) clinical adoption of these therapies. The manufacturing process consists of several complex steps that need to be coordinated and performed for the successful delivery of the therapy. Given the speedy turnaround required, manufacturing times need to be optimised and therefore, raw material availability is of high importance, particularly in the case of viral vectors that may have significant lead times. This is, however, a challenging task as resources are currently limited and the demand is unknown. Most of the decisions related to the manufacturing process need to be made prior to filing for regulatory approval, as they are considered fundamental for the therapy quality and efficacy and therefore cannot vary. Such matters refer to: (a) condition in which the therapy is transferred (fresh or cryopreserved), (b) choice of vector (lenti- or retro- vector) and (c) design of the process. Moreover, decisions on outsourcing and/or in-house processing for parts of the manufacturing line (e.g. quality control, storage) are required to be made well in advance and have a direct effect on the business and supply chain model. Unlike other (bio-) pharmaceutical products, the patient-specific character of CAR T cell manufacturing does not allow volumetric scale-up, posing additional challenges to the roadmap towards commercialisation. Currently, the commercially available therapies are offered at a significantly high cost, challenging the establishment of reimbursement procedures. Discussions are currently in place on how these therapies should be evaluated and reimbursed. There is a series of fundamental questions that need to be addressed, such as: (a) value of cure with focus on societal preferences, (b) choice of appropriate criteria for the evaluation of curative therapies, (c) appropriate characterization of uncertainty and (d) application of appropriate discount rates ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Hampson","given":"Grace","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"CAR-TCR Europe Summit 2018","id":"ITEM-1","issued":{"date-parts":[["2018"]]},"publisher":"Office of Health Economics","title":"Regenerative Medicines and Cell Therapy Products: Is the NICE Approach Fit for Purpose?","type":"paper-conference"},"uris":[""]}],"mendeley":{"formattedCitation":"(Hampson, 2018)","plainTextFormattedCitation":"(Hampson, 2018)","previouslyFormattedCitation":"(Hampson, 2018)"},"properties":{"noteIndex":0},"schema":""}(Hampson, 2018). Lastly, a wider clinical adoption of CAR T cell therapies will depend treatment efficiency and minimisation side effects. Other critical issues include chain-of-custody, as well as clinical responsibility.Autologous CAR T cells value chain: OpportunitiesUp to this point we have mainly discussed the challenges and risks encountered throughout the manufacturing and market establishment of CAR T cell therapies. The supply chain model that is currently in place can handle a finite and relatively low number of therapies and would require significant modifications to accommodate a larger patient population. Here we discuss the concept of a “dynamic” supply chain model that is designed to be adaptable based on the market needs. The concept of a “dynamic and flexible” supply chain The current supply chain model (small-scale v1) ( REF _Ref524335481 \h \* MERGEFORMAT Figure 8a) suggests that samples are transferred from the clinical site directly to the manufacturing facility and then they are shipped back to the hospital for administration. Nevertheless, the leukapheresis and administration procedures may happen at the same or different locations as presented in the small-scale v2 model ( REF _Ref524335481 \h \* MERGEFORMAT Figure 8b). Both models are currently used and are sustainable for the current patient numbers.Despite their good performance, these operational models entail a series of risks that will arise as patient numbers increase. Here we mention some of these risks:Cryopreservation/freezing & thawing: Based on expert opinion, these procedures are considered to be part of the manufacturing process and should be performed under the control and supervision of the manufacturer, in order to avoid complications with chain-of-custody. With a limited number of samples, these processes can be easily performed under a controlled environment at a clinical site, however, as patient numbers increase, it is likely that all or part of these procedures will need to take place at the manufacturing site or at a specialist 3rd party site, under the manufacturer’s control. Manufacturing capacity: Today, current facilities are able to handle the samples received from the leukapheresis centres. However, as the therapy becomes available to larger patient groups the need to process more therapies simultaneously (scale out) will become critical and therefore manufacturers will have to design a robust scheduling strategy and seek expansion opportunities (noting that volumetric scaling is not possible) in order to ensure that all samples are processed. Such an example is Kite Pharma’s new facility ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2019","5","2"]]},"author":[{"dropping-particle":"","family":"Blankenship","given":"Kyle","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2019"]]},"title":"Kite's CAR-T manufacturing gets another boost with new Maryland facility","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(Blankenship, 2019)","plainTextFormattedCitation":"(Blankenship, 2019)"},"properties":{"noteIndex":0},"schema":""}(Blankenship, 2019).Hospital capacity: At the final point, where the therapy is delivered at the hospital, it may need to wait until patient conditioning has been completed. Current facilities offer limited storage space and may not be equipped with liquid nitrogen storage units, challenging the scale up even more.Figure SEQ Figure \* ARABIC 68 Representation of the “dynamic” supply chain concept, where the supply chain network changes with respect to the increasing demand. Cases (a) and (b) represent the current state-of-the-art, where samples are collected from various hospitals and/or leukapheresis sites (respectively), transferred to the manufacturing facility and finally shipped to the hospital for administration. In (c) the extended v1 model is presented, where an intermediate collection point between the leukapheresis and the manufacturing site is established. Lastly, (d) presented the extended v2 model, where following the introduction of the intermediate collection point between the leukapheresis and the manufacturing site, we propose the introduction of a second intermediate point between the manufacturing facility and the hospital.The forecasted market growth ( REF _Ref519873557 \h \* MERGEFORMAT Figure 2) indicates that therapy demand will increase rapidly in the next few years, implying that manufacturers need to adapt their current manufacturing and/or distribution strategy in order to efficiently ensure adequate supply of therapies. Here we present for the first time the concept of a “dynamic” supply chain that is able to adapt to the demand, ensuring that all received samples are treated on time and therapies are delivered to the patient in a timely manner. As shown in REF _Ref524335481 \h \* MERGEFORMAT Figure 8c, the Extended v1 model, suggests that an intermediate site is established between the leukapheresis centre and the manufacturing facility (Intermediate Site 1). This could serve as a freezing and storage site that operates under the control of the manufacturer, responsible for the cryopreservation and storage of the therapies in processing. Being part of the manufacturer’s infrastructure, these sites can be controlled independently, giving the manufacturer greater freedom to schedule the logistics and shipments independently of hospital capacity and maintain control over the cryopreservation process. Nevertheless, patient scheduling remains one of the primary time constraints of highest priority. Similarly, the Extended v2 model ( REF _Ref524335481 \h \* MERGEFORMAT Figure 8d), proposes the introduction of a fifth location, to be established as an intermediate point between the manufacturing facility and the hospital, when the therapy is returned to the patient (Intermediate Site 2). The site could undertake storage and potentially thawing under a controlled environment, aiming to debottleneck capacity constraints at the hospital site and allow the manufacturer to have control over the thawing process. This could also be the same location as the Intermediate Site 1, simplifying the distribution network. The aforementioned models offer different levels of freedom to the manufacturer and are not mutually exclusive. As manufacturers prepare to respond to increased demand scenarios, they can start from the most centralised network ( REF _Ref524335481 \h \* MERGEFORMAT Figure 8a) and expand to the other configurations as the patient numbers and locations served increase. Furthermore, based on the demand forecasts the number of the facilities in use may increase (e.g. decentralised manufacturing using multiple facilities). Future decision-support requirementsconsiderationsSystematic decision making in autologous CAR T cell therapiesIn the previous section, we presented two novel types of supply chain concepts that aim to debottleneck both manufacturing and storage capacity issues. Although moving towards the establishment of such solutions will bring a step change in the current state-of-the-art, it will also be accompanied by significant increases in the capital and/or variable costs of the manufacturer. Moreover, decisions on the number and location of sites need to be taken prior to investment, ensuring that the model will be robust and result in an improved performance, while balancing the need to anticipate future demands and putting excessive capital at risk. Risk management and capacity optimisation in pharmaceutical supply chain models have been investigated by various research and industrial groups over the last few years. Some of the aspects of interest are: multi-product manufacturing ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/pchemeng.2012.03.002","abstract":"The management of global supply chains is highly complex and vital for multinational pharmaceutical enterprises. Global integrated planning in multi-site, multi-echelon network of a multinational company has attracted some academic interest. However, the focus has largely been on efficient solution strategies for large problems. In this work, we develop simple yet powerful MILP model for multi-period enterprise-wide planning. We represent the entire enterprise in a seamless fashion with a granularity of individual task campaigns on each production line. Our model integrates procurement, production, and distribution along with the effects of international tax differentials, inventory holding costs, material shelf-lives, waste treatment / disposal, and other real-life factors on the after-tax profit of the company. To demonstrate the performance of our model, we solve two example problems of planning multinational pharmaceutical enterprise. For our evaluation, we consider an industrial scale planning problem for a supply chain network consisting of 34 different entities and producing 9 different products, for a period of 5 years.","author":[{"dropping-particle":"","family":"Susarla","given":"Naresh","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karimi","given":"I A","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Computers and Chemical Engineering","id":"ITEM-1","issued":{"date-parts":[["2012"]]},"page":"168-177","title":"Integrated supply chain planning for multinational pharmaceutical enterprises","type":"article-journal","volume":"42"},"uris":[""]}],"mendeley":{"formattedCitation":"(Susarla and Karimi, 2012)","plainTextFormattedCitation":"(Susarla and Karimi, 2012)","previouslyFormattedCitation":"(Susarla and Karimi, 2012)"},"properties":{"noteIndex":0},"schema":""}(Susarla and Karimi, 2012), new product decisions/portfolio planning ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S0098-1354(00)00388-4","ISSN":"00981354","abstract":"One of the greatest challenges facing industry in the new century will be the process of selecting which new products to develop. Capital resources and manpower are limited. Stakeholders are demanding ever-increasing rates of return. These problems are especially difficult in highly regulated industries such as the chemicals and life sciences businesses, where development times are long and costly. In formative industries such as biotechnology, regulatory requirements continue to tighten, as public perception is often more influential than science in the approval process. Engineers are comfortable building process models, However, they infrequently think about the development of new products or the selection of new products as processes. This study is an attempt to get the engineers involved in the new product decision making process. Using the pharmaceutical industry as an example, probabilistic network models are used to capture all the activities and resources required in the 'process' of developing a new drug. The data representing each new product candidate are then combined into a simulation model of the new product development pipeline. This simulation model can be used by management to obtain insights into a new product portfolio, which will provide high rates of return at an acceptable level of exposure to risk for the corporation. (C) 2000 Elsevier Science Ltd.","author":[{"dropping-particle":"","family":"Blau","given":"Gary","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mehta","given":"Bharat","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bose","given":"Shantanu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pekny","given":"Joe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sinclair","given":"Gavin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Keunker","given":"Kay","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bunch","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Computers and Chemical Engineering","id":"ITEM-1","issue":"2-7","issued":{"date-parts":[["2000","7","15"]]},"page":"659-664","publisher":"Pergamon","title":"Risk management in the development of new products in highly regulated industries","type":"paper-conference","volume":"24"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1021/bp070410s","ISSN":"87567938","abstract":"Optimizing the structure and development pathway of biopharmaceutical drug portfolios are core concerns to the developer that come with several attached complexities. These include strategic decisions for the choice of drugs, the scheduling of critical activities, and the possible involvement of third parties for development and manufacturing at various stages for each drug. Additional complexities that must be considered include the impact of making such decisions in an uncertain environment. Presented here is the development of a stochastic multi-objective optimization framework designed to address these issues. The framework harnesses the ability of Bayesian networks to characterize the probabilistic structure of superior decisions via machine learning and evolve them to multi-objective optimality. Case studies that entailed three- and five-drug portfolios alongside a range of cash flow constraints were constructed to derive insight from the framework where results demonstrate that a variety of options exist for formulating nondominated strategies in the objective space considered, giving the manufacturer a range of pursuable options. In all cases limitations on cash flow reduce the potential for generating profits for a given probability of success. For the sizes of portfolio considered, results suggest that na?vely applying strategies optimal for a particular size of portfolio to a portfolio of another size is inappropriate. For the five-drug portfolio the most preferred means for development across the set of optimized strategies is to fully integrate development and commercial activities in-house. For the three-drug portfolio, the preferred means of development involves a mixture of in-house, outsourced, and partnered activities. Also, the size of the portfolio appears to have a larger impact on strategy and the quality of objectives than the magnitude of cash flow constraint.","author":[{"dropping-particle":"","family":"George","given":"Edmund D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Farid","given":"Suzanne S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Biotechnology Progress","id":"ITEM-2","issue":"3","issued":{"date-parts":[["2008","6","6"]]},"page":"698-713","publisher":"American Chemical Society (ACS)","title":"Strategic biopharmaceutical portfolio development: An analysis of constraint-induced implications","type":"article-journal","volume":"24"},"uris":[""]}],"mendeley":{"formattedCitation":"(Blau <i>et al.</i>, 2000; George and Farid, 2008)","plainTextFormattedCitation":"(Blau et al., 2000; George and Farid, 2008)","previouslyFormattedCitation":"(Blau <i>et al.</i>, 2000; George and Farid, 2008)"},"properties":{"noteIndex":0},"schema":""}(Blau et al., 2000; George and Farid, 2008), impact of innovative technologies on the supply chain ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/J.ORP.2017.05.002","ISSN":"2214-7160","abstract":"This research evaluates reconfiguration opportunities in Pharmaceutical Supply Chains (PSC) resulting from technology interventions in manufacturing, and new, more patient-centric delivery models. A critical synthesis of the academic and practice literature is used to identify, conceptualise, analyse and categorise PSC models. From a theoretical perspective, a systems view of operations research is adopted to provide insights on a broader range of OR activities, from conceptual to mathematical modelling and model solving, up to implementation. The research demonstrates that: 1) current definitions of the PSC are largely production-centric and fail to capture patient consumption, and hence healthcare outcomes; 2) most PSC mathematical models lack adequate conceptualisation of the structure and behaviour of the supply chain, and the boundary conditions that need to be considered for a given problem; 3) models do not adequately specify current unit operations or future production technology options, and are therefore unable to address the critical questions around alternative product or process technologies; 4) economic evaluations are limited to direct costing, rather than systemic approaches such as supply chain costing and total cost of ownership. While current models of the PSC may help with the optimisation of specific unit operations, their theoretical benefits could be offset by the dynamics of complex upstream (supply) and downstream (distribution and healthcare delivery) systems. To overcome these limitations, this research provides initial directions towards an integrated systems approach to PSC modelling. This perspective involves problem conceptualisation and boundary definition; design, formulation and solution of mathematical models, through to practical implementation of identified solutions. For both academics and practitioners, research findings suggest a systems approach to PSC modelling can provide improved conceptualisation and evaluation of alternative technologies, and supply network configuration options.","author":[{"dropping-particle":"","family":"Settanni","given":"Ettore","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Harrington","given":"Tomás Seosamh","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Srai","given":"Jagjit Singh","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Operations Research Perspectives","id":"ITEM-1","issued":{"date-parts":[["2017","1","1"]]},"page":"74-95","publisher":"Elsevier","title":"Pharmaceutical supply chain models: A synthesis from a systems view of operations research","type":"article-journal","volume":"4"},"uris":[""]}],"mendeley":{"formattedCitation":"(Settanni, Harrington and Srai, 2017)","plainTextFormattedCitation":"(Settanni, Harrington and Srai, 2017)","previouslyFormattedCitation":"(Settanni, Harrington and Srai, 2017)"},"properties":{"noteIndex":0},"schema":""}(Settanni, Harrington and Srai, 2017), as well as capacity and long-term planning ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1205/026387603322150516","ISBN":"0263-8762","ISSN":"02638762","abstract":"Market pressures and tough socio-political regulations are among the current factors that are changing the way in which the pharmaceutical business is operated. The pharmaceutical industries are faced with the question of the best use of the limited resources available to obtain the highest possible profit from a potential product portfolio. Thus, they are being forced to consider ever more systematic approaches to optimize their potential product portfolio. Here, we present a mathematical programming approach for the problem of capacity planning under clinical trials uncertainty. This optimization-based approach selects the final product portfolio and the production planning and investment strategy simultaneously subject to the uncertainty of the outcomes of the clinical trials for each potential drug. Four clinical trial outcomes (high success, target success, low success, failure) for each product are considered as is typical in the industry. As these outcomes have different probabilities of occurrence and the information from the trials will become available at different times, the investment problem becomes a large-scale, multistage, multiperiod stochastic optimization problem, which is then reformulated as a multiscenario, mixed integer linear programming (MILP) model. For this model, a performance measure that takes appropriate account of risk and potential returns has also been formulated. The applicability of the model is demonstrated by an illustrative example.","author":[{"dropping-particle":"","family":"Gatica","given":"G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Papageorgiou","given":"L G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shah","given":"N","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Chemical Engineering Research and Design","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2003"]]},"page":"665-678","title":"Capacity planning under uncertainty for the pharmaceutical industry","type":"article-journal","volume":"81"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1002/btpr.1860","ISSN":"15206033","abstract":"Production planning for biopharmaceutical portfolios becomes more complex when products switch between fed-batch and continuous perfusion culture processes. This paper describes the development of a mixed integer linear programming (MILP) model that incorporates both perfusion and fed-batch processes to optimise capacity plans for multiple products across multiple facilities to meet quarterly demands. Specific constraints have been created to capture challenges dealing with processes with different modes of operation such as sequence-dependent changeover times. The model is applied to an industrial case study to illustrate how the framework aids decisions regarding outsourcing capacity to third party manufacturers or building new facilities. The impact of uncertainties on the optimal production plans and costs is captured through the use of scenario analysis. ? 2012 Elsevier B.V.","author":[{"dropping-particle":"","family":"Siganporia","given":"Cyrus C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghosh","given":"Soumitra","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Daszkowski","given":"Thomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Papageorgiou","given":"Lazaros G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Farid","given":"Suzanne S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Biotechnology Progress","id":"ITEM-2","issue":"3","issued":{"date-parts":[["2014"]]},"page":"594-606","title":"Capacity planning for batch and perfusion bioprocesses across multiple biopharmaceutical facilities","type":"article-journal","volume":"30"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1021/bp0601950","ISSN":"87567938","abstract":"Manufacturers in the biopharmaceutical industry face greater scheduling and planning challenges as the trend of employing multiproduct manufacturing facilities continues to grow. These challenges are complicated by the randomness inherent in the biopharmaceutical manufacturing environment. This work focuses on capturing the effect of uncertainties in fermentation titres when optimising planning of biopharmaceutical manufacturing campaigns. In this paper we extend our previous deterministic medium term planning formulation to include uncertain production rates resulting in a two stage, multi-scenario, mixedinteger linear programming (MILP) model. When tested on industrial-sized problems, the resulting MILP problem proved intractable. An iterative solution algorithm is proposed for solving the resulting large scale MILP planning problem. The applicability of the algorithm is demonstrated through three illustrative examples. The computational results indicate that the proposed solution algorithm offers a significant reduction in the computational requirements whilst maintaining solution quality. The proposed optimisation-based framework presents an opportunity for biomanufacturers to make better medium term planning decisions, particularly under uncertain manufacturing conditions. ?? 2006 Elsevier B.V. All rights reserved.","author":[{"dropping-particle":"","family":"Lakhdar","given":"Kais","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Farid","given":"Suzanne S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Titchener-Hooker","given":"Nigel J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Papageorgiou","given":"Lazaros G.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Biotechnology Progress","id":"ITEM-3","issue":"6","issued":{"date-parts":[["2006","12","1"]]},"page":"1630-1636","publisher":"American Chemical Society (ACS)","title":"Medium term planning of biopharmaceutical manufacture with uncertain fermentation titers","type":"article-journal","volume":"22"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1021/bp0701362","ISSN":"87567938","abstract":"Biopharmaceutical companies with large portfolios of clinical and commercial products typically need to allocate production across several multiproduct facilities, including third party contract manufacturers. This poses several capacity planning challenges which are further complicated by the need to satisfy different stakeholders often with conflicting objectives. This work addresses the question of how a biopharmaceutical manufacturer can make better long-term capacity planning decisions given multiple strategic criteria such as cost, risk, customer service level, and capacity utilization targets. A long-term planning model that allows for multiple facilities and accounts for multiple objectives via goal programming is developed. An industrial case study based on a large scale biopharmaceutical manufacturer is used to illustrate the functionality of the model. A single objective model is used to identify how best to use existing capacity so as to maximize profits for different demand scenarios. Mitigating risk due to unforeseen circumstances by including a dual facility constraint is shown to be a reasonable strategy at base case demand levels but unacceptable if demands are 150% higher than expected. The capacity analysis identifies where existing capacity fails to meet demands given the constraints. A multiobjective model is used to demonstrate how key performance measures change given different decision making policies where different weights are assigned to cost, customer service level, and utilization targets. The analysis demonstrates that a high profit can still be achieved while meeting key targets more closely. The sensitivity of the optimal solution to different limits on the targets is illustrated.","author":[{"dropping-particle":"","family":"Lakhdar","given":"K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Savery","given":"J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Papageorgiou","given":"L. G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Farid","given":"S. S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Biotechnology Progress","id":"ITEM-4","issue":"6","issued":{"date-parts":[["2007","12","7"]]},"page":"1383-1393","publisher":"American Chemical Society (ACS)","title":"Multiobjective long-term planning of biopharmaceutical manufacturing facilities","type":"article-journal","volume":"23"},"uris":[""]}],"mendeley":{"formattedCitation":"(Gatica, Papageorgiou and Shah, 2003; Lakhdar <i>et al.</i>, 2006, 2007; Siganporia <i>et al.</i>, 2014)","plainTextFormattedCitation":"(Gatica, Papageorgiou and Shah, 2003; Lakhdar et al., 2006, 2007; Siganporia et al., 2014)","previouslyFormattedCitation":"(Gatica, Papageorgiou and Shah, 2003; Lakhdar <i>et al.</i>, 2006, 2007; Siganporia <i>et al.</i>, 2014)"},"properties":{"noteIndex":0},"schema":""}(Gatica, Papageorgiou and Shah, 2003; Lakhdar et al., 2006, 2007; Siganporia et al., 2014). It is therefore evident that “systems” approaches have been employed for the improvement and/or assistance of decision-making in the design of efficient distribution models of (bio)pharmaceuticals and will be relevant here.As we move towards more personalized medicines and particularly autologous therapies, the current supply chain models will have to adapt in order to meet both patient and provider expectations. Therefore, the pharmaceutical industry needs to re-think the one-type-fits-all model that is currently in place. In order to de-risk and smooth the transition, advanced mathematical modelling methods can be employed that can provide a low-cost test bed to run different configuration scenarios, such as the ones presented above. Constraints on processing times, capacity, as well as regulatory implications can be considered in the model structure so that the generated solutions comply with all pre-defined specifications. The discussed models are usually validated using real-world data from previous case studies, thus ensuring their credibility. Such solutions may also provide evidence that can be included in the dossier for regulatory approval, strengthening the application. Further uses of such models could be to evaluate the benefits of alternative emerging technologies such as allogeneic therapies, continuous processing, non-viral gene editing systems etc.Allogeneic CAR T cell therapiesWhile autologous CAR T cells have demonstrated remarkable results, efforts are made towards the successful development of allogeneic therapies, where the apheresis material comesis harvested from form healthy donors. Future marketed allogeneic therapies have the potential to significantly challenge the market share of autologous products. From a manufacturing and supply chain perspective, theallogeneic technologies can use a former will be looking into a “cell-bank” model, where the starting material is readily available to be manufactured, therefore reducing waiting times upstream of the supply chain and facilitating volumetric scaling. The reducing waiting times and potentially increased scales latter could be translated intolead to faster responses to the demand and therefore shorter return times of the overall therapy. ConclusionsAutologous therapies are expected to require new types of biopharmaceutical supply chain models as they stand today. Their patient-specific nature places patients as an integral part of the distribution network, thus challenging the design of generic supply chain models. Moreover, the uncertain market size and demand, as well as the limited supply of key raw materials, require the development of a detailed manufacturing and distribution plan that will be able to mitigate risks of failure. Furthermore, increasing demand cannot be met by volumetric scaling.Aiming to make the CAR T cell therapies scalable, responsive and more cost-effective, we describe novel supply chain concepts based on the introduction of intermediate collection points, aiming to mitigate risks related with storage capacity at the manufacturing and hospital sites. There are still various challenges and decisions that need to be tackled and answered, such as location/number of sites. Such questions can be addressed through systems approaches that employ modelling and optimisation methods in order to design equally good solutions that respond to all, sometimes conflicting, objectives.Acknowledgments The authors would like to acknowledge expert opinion received through multiple conversations with the User Steering Committee of the Future Targeted Healthcare Manufacturing Hub. Funding from the UK Engineering & Physical Sciences Research Council (EPSRC) for the Future Targeted Healthcare Manufacturing Hub hosted at University College London with UK university partners is gratefully acknowledged (Grant Reference: EP/P006485/1). Financial and in-kind support from the consortium of industrial users and sector organisations is also acknowledged. Conflict of interestThe authors declare no conflict of interest.ReferencesADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY Blankenship, K. (2019) Kite’s CAR-T manufacturing gets another boost with new Maryland facility. Available at: (Accessed: 2 May 2019).Blau, G. et al. (2000) ‘Risk management in the development of new products in highly regulated industries’, in Computers and Chemical Engineering. Pergamon, pp. 659–664. doi: 10.1016/S0098-1354(00)00388-4.Branke, J., Farid, S. S. and Shah, N. (2016) ‘Industry 4.0: a vision for personalized medicine supply chains?’, Cell and Gene Therapy Insights, 2(2), pp. 263–270. doi: 10.18609/cgti.2016.027.EMA (2018) First two CAR-T cell medicines recommended for approval in the European Union. Available at: (Accessed: 10 September 2018).FDA (2015a) Kymriah, Approved Risk Evaluation and Mitigation Strategies (REMS). Available at: (Accessed: 13 June 2018).FDA (2015b) Yescarta, Approved Risk Evaluation and Mitigation Strategies (REMS). Available at: (Accessed: 13 June 2018).Gatica, G., Papageorgiou, L. G. and Shah, N. (2003) ‘Capacity planning under uncertainty for the pharmaceutical industry’, Chemical Engineering Research and Design, 81(6), pp. 665–678. doi: 10.1205/026387603322150516.George, E. D. and Farid, S. S. (2008) ‘Strategic biopharmaceutical portfolio development: An analysis of constraint-induced implications’, Biotechnology Progress. American Chemical Society (ACS), 24(3), pp. 698–713. doi: 10.1021/bp070410s.Gill, S., Maus, M. V. and Porter, D. L. (2016) ‘Chimeric antigen receptor T cell therapy: 25 years in the making’, Blood Reviews. Elsevier B.V., 30(3), pp. 157–167. doi: 10.1016/j.blre.2015.10.003.Griffiths, R. and Lakelin, M. (2017) ‘Successfully managing the unique demands of cell therapy supply chains’, p. 9. Available at: (Accessed: 15 October 2018).Hampson, G. (2018) ‘Regenerative Medicines and Cell Therapy Products: Is the NICE Approach Fit for Purpose?’, in CAR-TCR Europe Summit 2018. Office of Health Economics.Herbert, S. (2017) ‘Understanding the Critical Impact of Logistics on Scale-Up &amp; Commercialization’, Cell and Gene Therapy Insights, 3(8), pp. 655–662. doi: 10.18609/cgti.2017.067.HMRN (Haematological Malignancy Research Network) (2018) Statistics, Epidemiology & Cancer Statistics Group Department of Health Sciences Area 3 Seebohm Rowntree Building YORK YO10 5DD. Available at: (Accessed: 20 July 2018).Jackson, H. J., Rafiq, S. and Brentjens, R. J. (2016) ‘Driving CAR T-cells forward’, Nature Reviews Clinical Oncology. Nature Publishing Group, 13(6), pp. 370–383. doi: 10.1038/nrclinonc.2016.36.Kaiser, A. D. et al. (2015) ‘Towards a commercial process for the manufacture of genetically modified T cells for therapy’, Cancer Gene Therapy. Nature Publishing Group, 22(2), pp. 72–78. doi: 10.1038/cgt.2014.78.Khalil, D. N. et al. (2016) ‘The future of cancer treatment: Immunomodulation, CARs and combination immunotherapy’, Nature Reviews Clinical Oncology. NIH Public Access, 13(5), pp. 273–290. doi: 10.1038/nrclinonc.2016.25.Kite Pharma (2018) First CAR T Therapy for Certain Types of Relapsed or Refractory B-Cell Lymphoma. Available at: (Accessed: 7 March 2019).Lakhdar, K. et al. (2006) ‘Medium term planning of biopharmaceutical manufacture with uncertain fermentation titers’, Biotechnology Progress. American Chemical Society (ACS), 22(6), pp. 1630–1636. doi: 10.1021/bp0601950.Lakhdar, K. et al. (2007) ‘Multiobjective long-term planning of biopharmaceutical manufacturing facilities’, Biotechnology Progress. American Chemical Society (ACS), 23(6), pp. 1383–1393. doi: 10.1021/bp0701362.Lamb, M., Margolin, R. E. and Vitale, J. (2017) ‘Personalized Supply Chains for Cell Therapies’, Cell and Gene Therapy Insights, 3(10), pp. 815–833. doi: 10.18609/cgti.2017.081.Levine, B. L. (2015) ‘Performance-enhancing drugs: Design and production of redirected chimeric antigen receptor (CAR) T cells’, Cancer Gene Therapy. Nature Publishing Group, 22(2), pp. 79–84. doi: 10.1038/cgt.2015.5.Levine, B. L. et al. (2017) ‘Global Manufacturing of CAR T Cell Therapy’, Molecular Therapy - Methods and Clinical Development. Elsevier Ltd., 4(March), pp. 92–101. doi: 10.1016/j.omtm.2016.12.006.Maude, S. L. et al. (2018) ‘Tisagenlecleucel in Children and Young Adults with B-Cell Lymphoblastic Leukemia’, New England Journal of Medicine. Massachusetts Medical Society, 378(5), pp. 439–448. doi: 10.1056/NEJMoa1709866.N.I.H U.S National Library of Medicine (2018) . Available at: (Accessed: 23 July 2018).Neelapu, S. S. et al. (2017) ‘Axicabtagene Ciloleucel CAR T-Cell Therapy in Refractory Large B-Cell Lymphoma’, New England Journal of Medicine. Massachusetts Medical Society, 377(26), p. NEJMoa1707447. doi: 10.1056/NEJMoa1707447.Novartis (2018a) Kymriah? (tisagenlecleucel), first-in-class CAR-T therapy from Novartis, receives second FDA approval to treat appropriate r/r patients with large B-cell lymphoma. Available at: (Accessed: 24 July 2018).Novartis (2018b) KYMRIAH Treatment Process, Dosing &amp; Administration | HCP. Available at: (Accessed: 7 March 2019).Pantelides, C. C. (1994) ‘Unified frameworks for optimal process planning and scheduling’, in Second conference on foundations of computer aided operations. New York: Cache Publications, pp. 253–274.Papathanasiou, M. (2018) ‘Advances in Enabling Smart Technologies across the Cell Therapy Supply Chain’, Cell and Gene Therapy Insights, 4(5), pp. 495–500. doi: 10.18609/cgti.2018.050.Sadelain, M. et al. (2015) ‘CD19 CAR Therapy for Acute Lymphoblastic Leukemia’, American Society of Clinical Oncology Educational Book, 35, pp. e360–e363. doi: 10.14694/EdBook_AM.2015.35.e360.Settanni, E., Harrington, T. S. and Srai, J. S. (2017) ‘Pharmaceutical supply chain models: A synthesis from a systems view of operations research’, Operations Research Perspectives. Elsevier, 4, pp. 74–95. doi: 10.1016/J.ORP.2017.05.002.Shah, N. (2004) ‘Pharmaceutical supply chains: Key issues and strategies for optimisation’, in Computers and Chemical Engineering. Pergamon, pp. 929–941. doi: 10.1016/pchemeng.2003.09.022.Siganporia, C. C. et al. (2014) ‘Capacity planning for batch and perfusion bioprocesses across multiple biopharmaceutical facilities’, Biotechnology Progress, 30(3), pp. 594–606. doi: 10.1002/btpr.1860.Susarla, N. and Karimi, I. A. (2012) ‘Integrated supply chain planning for multinational pharmaceutical enterprises’, Computers and Chemical Engineering, 42, pp. 168–177. doi: 10.1016/pchemeng.2012.03.002.U.S. Food and Drug Administration (2017) ‘Summary Basis for Regulatory Action- ATryn’, pp. 1–16. Available at: (Accessed: 24 July 2018).Uk, C. R. (2014) Worldwide cancer statistics, Cancer research UK. Available at: (Accessed: 12 June 2018).UN (2018) United Nations Population Division.Appendix AFor the patient population forecast studies, we assume a constant average growth of approximately 0.4% (ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"UN","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2018"]]},"title":"United Nations Population Division","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(UN, 2018)","plainTextFormattedCitation":"(UN, 2018)","previouslyFormattedCitation":"(UN, 2018)"},"properties":{"noteIndex":0},"schema":""}(UN, 2018)) and constant incidence rates per 100,000 people (ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2018","7","20"]]},"author":[{"dropping-particle":"","family":"HMRN (Haematological Malignancy Research Network)","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Epidemiology & Cancer Statistics Group Department of Health Sciences Area 3 Seebohm Rowntree Building YORK YO10 5DD","id":"ITEM-1","issued":{"date-parts":[["2018"]]},"title":"Statistics","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(HMRN (Haematological Malignancy Research Network), 2018)","plainTextFormattedCitation":"(HMRN (Haematological Malignancy Research Network), 2018)","previouslyFormattedCitation":"(HMRN (Haematological Malignancy Research Network), 2018)"},"properties":{"noteIndex":0},"schema":""}(HMRN (Haematological Malignancy Research Network), 2018)), as presented in Table A.1 REF _Ref519863135 \h \* MERGEFORMAT Table 3. Based on these assumptions, we present indicative projections (Table A.21) for the liquid cancer patient population in the UK for the next 15 years. In order to estimate the patient population, we only consider liquid cancer types as currently CAR T therapies are considered to be more advanced in that space. Furthermore, the types of liquid cancer here have been chosen based on data availability. Patient population numbers for types not present in the current set are either scarcely provided or not available. Table A.1 Annual incidence rate per 100,000 for liquid cancer cases in the UK based on HMRN reports.DiseaseAnnual rate per 100,000Acute myeloid leukaemia3.9B-lymphoblastic leukaemia0.9Chronic lymphocytic leukaemia6.6Non-Hodgkin lymphoma16.8Marginal zone lymphoma3.5Follicular lymphoma3.1Mantle cell lymphoma0.8Diffuse large B-cell lymphoma8.1Burkitt lymphoma0.3Hodgkin lymphoma2.8Classical Hodgkin lymphoma2.4Lymphocyte predominant nodular Hodgkin lymphoma0.3Myeloma6.5Monoclonal B-cell lymphocytosis2.5Myelodysplastic syndromes3.5Table A.12 Population forecast studies for the UK population and liquid cancer patient population for the next 15 years.The three final rows of Table A.21 represent: (1) the total number of patients with liquid cancer in the UK for the next 15 years, (2) a scenario where estimates are 20% lower and (3) a scenario where estimates are 20% higher. REF _Ref519873557 \h \* MERGEFORMAT Figure 2, included in the main body, represents the ±20% case. Nevertheless, CAR T cell therapies will most probably not be the first line of therapy, therefore limiting the number of eligible patients. REF _Ref519873557 \h \* MERGEFORMAT Figure A.1 illustrates a potential scenario, where only 10% of the total patient population will be eligible for CAR T cell therapy treatment. Despite this constraint, patient numbers are estimated to increase almost fivefold by 2031, thus challenging CART manufacturing and supply chain scale up. Figure A.1 Forecast scenario of patient population eligible to receive CAR T cell therapies by 2031. Calculations consider that the total UK population will be 70,629,653 by 2031 and fixed annual incident rates as shown in REF _Ref519863135 \h \* MERGEFORMAT Table 3 and a ±20% variation. ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download