Current practices for QSP model assessment: an IQ consortium survey

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.

In‐depth analysis of patterns in selection of different physiologically based pharmacokinetic modeling tools: Part I – Applications and rationale behind the use of open source‐code software

“…However, considering ‘open’ as ‘exposed’ and ‘sensitive’ to interference and requiring extensive quality checks in every use case may explain why the universal uptake of open science‐backed approaches has been overwhelmingly slow (Heise & Pearce, 2020) and beyond arguments related to intellectual property etc. It should be emphasised that debates regarding open science should be kept apart from debates concerning OSC versus NOSC as the two are fundamentally different (Chan et al., 2022). Unfortunately, some reports fail to distinguish between the two concepts and misquote the call for debate on pros and cons of OSC/NOSC with the requirements for open science (Chan et al., 2022; Rostami‐Hodjegan & Bois, 2021) whilst the latter is generally accepted by the overwhelming majority of researchers and does not require any debate.…”

“…It should be emphasised that debates regarding open science should be kept apart from debates concerning OSC versus NOSC as the two are fundamentally different (Chan et al., 2022). Unfortunately, some reports fail to distinguish between the two concepts and misquote the call for debate on pros and cons of OSC/NOSC with the requirements for open science (Chan et al., 2022; Rostami‐Hodjegan & Bois, 2021) whilst the latter is generally accepted by the overwhelming majority of researchers and does not require any debate.…”

“…The topic of 'Open Science' is not novel and can be traced back to the early 17th century, when the idea was a distinctive aspect of the Scientific Revolution, making science accessible to a broader audience and signalling a break from the previously dominant ethos of secrecy (David, 2007) (Chan et al, 2022). Unfortunately, some reports fail to distinguish between the two concepts and misquote the call for debate on pros and cons of OSC/NOSC with the requirements for open science (Chan et al, 2022;Rostami-Hodjegan & Bois, 2021) whilst the latter is generally accepted by the overwhelming majority of researchers and does not require any debate.…”

Considerations for Industry—Preparing for the FDA Model‐Informed Drug Development (MIDD) Paired Meeting Program

“…” Regulatory and industrial scientists further elaborate that MIDD approaches can support a wide variety of applications in drug development and regulatory review, such as for dose optimization and clinical trial design, and in providing evidence of clinical effectiveness 2–5 . Indeed, modeling and simulation spans the entire spectrum of discovery, development, and review of new drugs and is sometimes referred to as MID3: model‐informed drug discovery and development 5–8 …”

Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices

“…[7][8][9] However, with multidimensional, multisource, and multimodal data being generated, 10 there is a need to embrace modern predictive and computationally powerful analytics as a next step in the evolution of PMx methodologies. 6 Just as the significant increase in the adoption of Quantitative Systems Pharmacology (QSP) in regulatory submissions since 2013 11 prompted a consortium collaboration to evaluate the state of model assessment in the pharmaceutical/biotech industry and provide recommendations for its advancement, 12 we have recognized a similar emerging need within the scientific communities for AI/ML. There is a growing demand to emphasize the potential of AI/ML in the realm of drug discovery and development, whereas also acknowledging and addressing the associated challenges.…”