Opportunities and Challenges of Disease Progression Modeling in Drug Development – An IQ Perspective

Author:

Goteti Kosalaram1ORCID,Hanan Nathan2,Magee Mindy2,Wojciechowski Jessica3,Mensing Sven4ORCID,Lalovic Bojan5,Hang Yaming6,Solms Alexander7ORCID,Singh Indrajeet8,Singh Rajendra9,Rieger Theodore Robert10ORCID,Jin Jin Y.11

Affiliation:

1. Quantitative Pharmacology, EMD Serono Research and Development Institute, Inc. Billerica Massachusetts USA

2. Clinical Pharmacology Modeling and Simulation GlaxoSmithKline Collegeville Pennsylvania USA

3. Global Product Development Pfizer Inc. Groton Connecticut USA

4. Clinical Pharmacology AbbVie Deutschland GmbH & Co. KG Ludwigshafen Germany

5. Clinical Pharmacology Modeling and Simulation Eisai Inc Nutley New Jersey USA

6. Quantitative Clinical Pharmacology Takeda Cambridge Massachusetts USA

7. Clinical Pharmacometrics/Modeling & Simulation Bayer AG Berlin Germany

8. Clinical Pharmacology, Gilead Sciences Foster City California USA

9. Teva Pharmaceuticals West Chester Pennsylvania USA

10. Worldwide Research, Development and Medical Pfizer Inc. Cambridge Massachusetts USA

11. Clinical Pharmacology Genentech, Inc. South San Francisco California USA

Abstract

Disease progression modeling (DPM) represents an important model‐informed drug development framework. The scientific communities support the use of DPM to accelerate and increase efficiency in drug development. This article summarizes International Consortium for Innovation & Quality (IQ) in Pharmaceutical Development mediated survey conducted across multiple biopharmaceutical companies on challenges and opportunities for DPM. Additionally, this summary highlights the viewpoints of IQ from the 2021 workshop hosted by the US Food and Drug Administration (FDA). Sixteen pharmaceutical companies participated in the IQ survey with 36 main questions. The types of questions included single/multiple choice, dichotomous, rank questions, and open‐ended or free text. The key results show that DPM has different representation, it encompasses natural disease history, placebo response, standard of care as background therapy, and can even be interpreted as pharmacokinetic/pharmacodynamic modeling. The most common reasons for not implementing DPM as frequently seem to be difficulties in internal cross‐functional alignment, lack of knowledge of disease/data, and time constraints. If successfully implemented, DPM can have an impact on dose selection, reduction of sample size, trial read‐out support, patient selection/stratification, and supportive evidence for regulatory interactions. The key success factors and key challenges of disease progression models were highlighted in the survey and about 24 case studies across different therapeutic areas were submitted from various survey sponsors. Although DPM is still evolving, its current impact is limited but promising. The success of such models in the future will depend on collaboration, advanced analytics, availability of and access to relevant and adequate‐quality data, collaborative regulatory guidance, and published examples of impact.

Publisher

Wiley

Subject

Pharmacology (medical),Pharmacology

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