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Galluppi GR, Ahamadi M, Bhattacharya S, Budha N, Gheyas F, Li CC, Chen Y, Dosne AG, Kristensen NR, Magee M, Samtani MN, Sinha V, Taskar K, Upreti VV, Yang J, Cook J. Considerations for Industry-Preparing for the FDA Model-Informed Drug Development (MIDD) Paired Meeting Program. Clin Pharmacol Ther 2024; 116:282-288. [PMID: 38519861 DOI: 10.1002/cpt.3245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/01/2024] [Indexed: 03/25/2024]
Abstract
A recent industry perspective published in this journal describes the benefits received by drug companies from participation in the MIDD Pilot Program. Along with the primary objectives of supporting good decision-making in drug development, there were substantial savings in time and development costs. Furthermore, many sponsors reported qualitative benefits such as new learnings and clarity on MIDD strategies and methodology that could be applied to other development programs. Based on the success of the Pilot Program, the FDA recently announced the continuation of the MIDD Paired Meeting Program as part of the Prescription Drug User Fee Act (PDUFA VII). In this report, we describe the collective experiences of industry participants in the MIDD Program to date, including all aspects of the process from meeting request submission to follow-up actions. The purpose is to provide future participants with information to optimize the value of the MIDD Program.
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Affiliation(s)
| | | | | | | | | | - Chi-Chung Li
- Genentech Inc, South San Francisco, California, USA
| | - Yuan Chen
- Genentech Inc, South San Francisco, California, USA
| | - Anne-Gaëlle Dosne
- The Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Belgium
| | | | | | | | - Vikram Sinha
- Novartis Institute of Biomedical Research, Berwyn, Pennsylvania, USA
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2
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Venkatakrishnan K, Jayachandran P, Seo SK, van der Graaf PH, Wagner JA, Gupta N. Moving the Needle for Oncology Dose Optimization: A Call for Action. Clin Pharmacol Ther 2024; 115:1187-1197. [PMID: 38736240 DOI: 10.1002/cpt.3263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 04/05/2024] [Indexed: 05/14/2024]
Affiliation(s)
| | | | - Shirley K Seo
- Division of Cardiometabolic and Endocrine Pharmacology, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | | | - Neeraj Gupta
- Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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3
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Venkatakrishnan K, Jayachandran P, Seo SK, van der Graaf PH, Wagner JA, Gupta N. Moving the needle for oncology dose optimization: A call for action. CPT Pharmacometrics Syst Pharmacol 2024; 13:909-918. [PMID: 38778466 PMCID: PMC11179700 DOI: 10.1002/psp4.13157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 04/26/2024] [Indexed: 05/25/2024] Open
Affiliation(s)
| | | | - Shirley K Seo
- Division of Cardiometabolic and Endocrine Pharmacology, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | | | - Neeraj Gupta
- Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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4
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Venkatakrishnan K, Jayachandran P, Seo SK, van der Graaf PH, Wagner JA, Gupta N. Moving the needle for oncology dose optimization: A call for action. Clin Transl Sci 2024; 17:e13859. [PMID: 38923292 PMCID: PMC11196242 DOI: 10.1111/cts.13859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Affiliation(s)
| | | | - Shirley K. Seo
- Division of Cardiometabolic and Endocrine Pharmacology, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and ResearchU.S. Food and Drug AdministrationSilver SpringMarylandUSA
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Terranova N, Renard D, Shahin MH, Menon S, Cao Y, Hop CECA, Hayes S, Madrasi K, Stodtmann S, Tensfeldt T, Vaddady P, Ellinwood N, Lu J. Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices. Clin Pharmacol Ther 2024; 115:658-672. [PMID: 37716910 DOI: 10.1002/cpt.3053] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Abstract
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.
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Affiliation(s)
- Nadia Terranova
- Quantitative Pharmacology, Merck KGaA, Lausanne, Switzerland
| | - Didier Renard
- Full Development Pharmacometrics, Novartis Pharma AG, Basel, Switzerland
| | | | - Sujatha Menon
- Clinical Pharmacology, Pfizer Inc., Groton, Connecticut, USA
| | - Youfang Cao
- Clinical Pharmacology and Translational Medicine, Eisai Inc., Nutley, New Jersey, USA
| | | | - Sean Hayes
- Quantitative Pharmacology & Pharmacometrics, Merck & Co. Inc., Rahway, New Jersey, USA
| | - Kumpal Madrasi
- Modeling & Simulation, Sanofi, Bridgewater, New Jersey, USA
| | - Sven Stodtmann
- Pharmacometrics, AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany
| | | | - Pavan Vaddady
- Quantitative Clinical Pharmacology, Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA
| | | | - James Lu
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
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6
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Cheung SYA, Hay JL, Lin YW, de Greef R, Bullock J. Pediatric oncology drug development and dosage optimization. Front Oncol 2024; 13:1235947. [PMID: 38348118 PMCID: PMC10860405 DOI: 10.3389/fonc.2023.1235947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 12/29/2023] [Indexed: 02/15/2024] Open
Abstract
Oncology drug discovery and development has always been an area facing many challenges. Phase 1 oncology studies are typically small, open-label, sequential studies enrolling a small sample of adult patients (i.e., 3-6 patients/cohort) in dose escalation. Pediatric evaluations typically lag behind the adult development program. The pediatric starting dose is traditionally referenced on the recommended phase 2 dose in adults with the incorporation of body size scaling. The size of the study is also small and dependent upon the prevalence of the disease in the pediatric population. Similar to adult development, the dose is escalated or de-escalated until reaching the maximum tolerated dose (MTD) that also provides desired biological activities or efficacy. The escalation steps and identification of MTD are often rule-based and do not incorporate all the available information, such as pharmacokinetic (PK), pharmacodynamic (PD), tolerability and efficacy data. Therefore, it is doubtful if the MTD approach is optimal to determine the dosage. Hence, it is important to evaluate whether there is an optimal dosage below the MTD, especially considering the emerging complexity of combination therapies and the long-term tolerability and safety of the treatments. Identification of an optimal dosage is also vital not only for adult patients but for pediatric populations as well. Dosage-finding is much more challenging for pediatric populations due to the limited patient population and differences among the pediatric age range in terms of maturation and ontogeny that could impact PK. Many sponsors defer the pediatric strategy as they are often perplexed by the challenges presented by pediatric oncology drug development (model of action relevancy to pediatric population, budget, timeline and regulatory requirements). This leads to a limited number of approved drugs for pediatric oncology patients. This review article provides the current regulatory landscape, incentives and how they impact pediatric drug discovery and development. We also consider different pediatric cancers and potential clinical trial challenges/opportunities when designing pediatric clinical trials. An outline of how quantitative methods such as pharmacometrics/modelling & simulation can support the dosage-finding and justification is also included. Finally, we provide some reflections that we consider helpful to accelerate pediatric drug discovery and development.
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Ibrahim EIK, Ellingsen EB, Mangsbo SM, Friberg LE. Bridging responses to a human telomerase reverse transcriptase-based peptide cancer vaccine candidate in a mechanism-based model. Int Immunopharmacol 2024; 126:111225. [PMID: 37988911 DOI: 10.1016/j.intimp.2023.111225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 11/10/2023] [Accepted: 11/10/2023] [Indexed: 11/23/2023]
Abstract
Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed-effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage.
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Affiliation(s)
| | - Espen B Ellingsen
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway; Ultimovacs ASA, Oslo, Norway
| | - Sara M Mangsbo
- Department of Pharmacy, Uppsala University, Uppsala, Sweden; Ultimovacs AB, Uppsala, Sweden
| | - Lena E Friberg
- Department of Pharmacy, Uppsala University, Uppsala, Sweden.
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8
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Yao Y, Wang Z, Yong L, Yao Q, Tian X, Wang T, Yang Q, Hao C, Zhou T. Longitudinal and time‐to‐event modeling for prognostic implications of radical surgery in retroperitoneal sarcoma. CPT Pharmacometrics Syst Pharmacol 2022; 11:1170-1182. [PMID: 35758865 PMCID: PMC9469699 DOI: 10.1002/psp4.12835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/12/2022] [Accepted: 06/02/2022] [Indexed: 11/11/2022] Open
Affiliation(s)
- Ye Yao
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System Department of Pharmaceutics School of Pharmaceutical Sciences Peking University Beijing China
| | - Zhen Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) Department of Hepato‐Pancreato‐Biliary Surgery Sarcoma Center, Peking University Cancer Hospital and Institute Beijing China
| | - Ling Yong
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System Department of Pharmaceutics School of Pharmaceutical Sciences Peking University Beijing China
| | - Qingyu Yao
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System Department of Pharmaceutics School of Pharmaceutical Sciences Peking University Beijing China
| | - Xiuyun Tian
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) Department of Hepato‐Pancreato‐Biliary Surgery Sarcoma Center, Peking University Cancer Hospital and Institute Beijing China
| | - Tianyu Wang
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System Department of Pharmaceutics School of Pharmaceutical Sciences Peking University Beijing China
| | - Qirui Yang
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System Department of Pharmaceutics School of Pharmaceutical Sciences Peking University Beijing China
| | - Chunyi Hao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) Department of Hepato‐Pancreato‐Biliary Surgery Sarcoma Center, Peking University Cancer Hospital and Institute Beijing China
| | - Tianyan Zhou
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System Department of Pharmaceutics School of Pharmaceutical Sciences Peking University Beijing China
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Chan JR, Allen R, Boras B, Cabal A, Damian V, Gibbons FD, Gulati A, Hosseini I, Kearns JD, Saito R, Cucurull-Sanchez L, Selimkhanov J, Stein AM, Umehara K, Wang G, Wang W, Neves-Zaph S. Current practices for QSP model assessment: an IQ consortium survey. J Pharmacokinet Pharmacodyn 2022:10.1007/s10928-022-09811-1. [PMID: 35953664 PMCID: PMC9371373 DOI: 10.1007/s10928-022-09811-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/03/2022] [Indexed: 12/02/2022]
Abstract
Quantitative Systems Pharmacology (QSP) modeling is increasingly applied in the pharmaceutical industry to influence decision making across a wide range of stages from early discovery to clinical development to post-marketing activities. Development of standards for how these models are constructed, assessed, and communicated is of active interest to the modeling community and regulators but is complicated by the wide variability in the structures and intended uses of the underlying models and the diverse expertise of QSP modelers. With this in mind, the IQ Consortium conducted a survey across the pharmaceutical/biotech industry to understand current practices for QSP modeling. This article presents the survey results and provides insights into current practices and methods used by QSP practitioners based on model type and the intended use at various stages of drug development. The survey also highlights key areas for future development including better integration with statistical methods, standardization of approaches towards virtual populations, and increased use of QSP models for late-stage clinical development and regulatory submissions.
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Affiliation(s)
- Jason R Chan
- Global PKPD and Pharmacometrics, Eli Lilly and Company, Indianapolis, IN, 46285, USA.
| | - Richard Allen
- Worldwide Research, Development and Medical, Pfizer Inc. Kendall Square, Cambridge, MA, 02139, USA
| | - Britton Boras
- Worldwide Research, Development and Medical, Pfizer Inc.,, La Jolla, CA, 92121, USA
| | | | | | | | | | | | - Jeffrey D Kearns
- Novartis Institutes for BioMedical Research, Cambridge, MA, 02139, USA
| | - Ryuta Saito
- Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Yokohama, Japan
| | | | | | - Andrew M Stein
- Pharmacometrics, Novartis Institutes of BioMedical Research, Cambridge, MA, USA
| | - Kenichi Umehara
- Roche Pharmaceutical Research and Early Development, Basel, Switzerland
| | - Guanyu Wang
- Drug Metabolism and Pharmacokinetics, Vertex Pharmaceuticals, Abingdon, Oxfordshire, UK
| | - Weirong Wang
- Clinical Pharmacology and Pharmacometrics, Janssen Research and Development, LLC, Spring House, PA, 19477, USA
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Desikan R, Padmanabhan P, Kierzek AM, van der Graaf PH. Mechanistic Models of COVID-19: Insights into Disease Progression, Vaccines, and Therapeutics. Int J Antimicrob Agents 2022; 60:106606. [PMID: 35588969 PMCID: PMC9110059 DOI: 10.1016/j.ijantimicag.2022.106606] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/27/2022] [Accepted: 05/08/2022] [Indexed: 12/02/2022]
Abstract
The COVID-19 pandemic has severely impacted health systems and economies worldwide. Significant global efforts are therefore ongoing to improve vaccine efficacies, optimize vaccine deployment, and develop new antiviral therapies to combat the pandemic. Mechanistic viral dynamics and quantitative systems pharmacology models of SARS-CoV-2 infection, vaccines, immunomodulatory agents, and antiviral therapeutics have played a key role in advancing our understanding of SARS-CoV-2 pathogenesis and transmission, the interplay between innate and adaptive immunity to influence the outcomes of infection, effectiveness of treatments, mechanisms and performance of COVID-19 vaccines, and the impact of emerging SARS-CoV-2 variants. Here, we review some of the critical insights provided by these models and discuss the challenges ahead.
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Affiliation(s)
- Rajat Desikan
- Quantitative Systems Pharmacology (QSP) group, Certara, Sheffield and Canterbury, United Kingdom.
| | - Pranesh Padmanabhan
- Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Andrzej M Kierzek
- Quantitative Systems Pharmacology (QSP) group, Certara, Sheffield and Canterbury, United Kingdom; School of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom
| | - Piet H van der Graaf
- Quantitative Systems Pharmacology (QSP) group, Certara, Sheffield and Canterbury, United Kingdom; Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
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Arsène S, Couty C, Faddeenkov I, Go N, Granjeon-Noriot S, Šmít D, Kahoul R, Illigens B, Boissel JP, Chevalier A, Lehr L, Pasquali C, Kulesza A. Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions. Nat Commun 2022; 13:1980. [PMID: 35418135 PMCID: PMC9008035 DOI: 10.1038/s41467-022-29534-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 03/21/2022] [Indexed: 02/07/2023] Open
Abstract
Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden.
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Affiliation(s)
| | | | | | | | | | | | | | - Ben Illigens
- Novadiscovery SA, Lyon, France
- Dresden International University, Dresden, Germany
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12
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van der Graaf PH, Gerding AB. Themes in Clinical Pharmacology and Therapeutics. Clin Pharmacol Ther 2021; 110:1413-1415. [PMID: 34773711 DOI: 10.1002/cpt.2436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 11/11/2022]
Affiliation(s)
| | - Alethea B Gerding
- American Society for Clinical Pharmacology and Therapeutics, Alexandria, Virginia, USA
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