Model‐Informed Drug Development: Connecting the Dots With a Totality of Evidence Mindset to Advance Therapeutics

Abstract

The Merriam-Webster dictionary offers the essential meaning of a model as "a usually small copy of something," or "a set of ideas and numbers that describe the past, present, or future state of something." Model-informed drug development (MIDD) involves the strategic creation and integration of mathematical models for knowledge management, predictions, and decision making in pharmaceutical research and development. These models can "connect the dots" across various inputs related to drug properties, disease biology, patient characteristics and trial designs, transforming data to knowledge, enhancing efficiency in drug discovery and development, and maximizing therapeutic potential. This issue of Clinical Pharmacology and Therapeutics (CPT) includes Original Research Articles, Reviews, and Perspectives illustrating recent advances in established and emerging areas of MIDD, highlighting opportunities for interdisciplinary and cross-sector collaboration for enhancing impact.

Current practices for QSP model assessment: an IQ consortium survey

“…Dose selection and competitive differentiation are currently the most notable uses for QSP models, consistent with the prevalence of applications in the preclinical-Phase 1-Phase 2 space (Supplemental Material, Q10). This supports the notion that informing dose selection in the first patient study may be a primary application for QSP, similar to how drug-drug interaction (DDI) assessment is an influential and widely used application of PBPK [19]. Indeed, many of the examples presented at a recent ISOP/ FDA scientific exchange were focused on dose decisions [3].…”

Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions

“… Impact on trial feasibility Recommendation for the trialist Specific risk mitigation measures Weak (leading to disease burden change similar as year-to-year fluctuations) Assessment of clinical benefit is difficult with low number of events Reinforce and underline clinical significance of the demonstrated effect • Select population/endpoints where a smaller (absolute) effect on RTI prophylaxis is still clinically meaningful (characterized by small minimally important difference). One example is to focus on prophylaxis of viral infection induced wheezing or asthma exacerbations, see 70 , 71 , rather than upper RTI (mostly common cold) in the general population • Comprehensive reporting of rates, relative, and absolute benefit • Include secondary endpoints that add a diversified and multifaceted view to the clinical significance for assessors of the trial results (e.g., symptom-free days as RTI duration related endpoint) • Seek regulator’s feedback on the study protocol and statistical analysis plan with respect to clinical benefit assessment Medium (leading to substantially lower disease burden; magnitude of change with respect to average exceeds year-to-year fluctuations) Reduced post-hoc power with fixed sample size and less available patients that suffer from fixed minimum number of episodes Mitigate loss of power through sample size adjustment, adaptive trial design, and statistical analysis tailored to rare events • Multi-center trials with access to a larger patient pool can facilitate recruitment of larger sample sizes under difficult conditions • Use Model Informed Drug Development (MIDD) to leverage the totality of evidence for an optimal trial design and extrapolation 72 , 73 • Primary endpoint analysis based on event rate ratio (ERR) and accounting for excess zeros, e.g., zero-inflated negative binomial regression (ZINB) in frame of generalized linear models (GLM) 74 , 75 • Use trial monitoring and (Bayesian) adaptive trial design 76 especially sample size reestimation (increasing the sample size based on interim data analysis) 77 , group sequential designs 78 (trials can be stopped early once significant results are obtained, or the trial can be stopped for futility) • Seek regulator’s feedback on any modeling and simulation methods applied (e.g., FDA’s MIDD pilot program) 79 , for complex innovative trial design and the statistical analysis (e.g., FDA’s complex innovative trial design pilot program 80 ) Strong = lockdown (leading to attenuation of seasonal epidemic) High risk of insufficient sample size and severe recruitment issues Change the development plan • Change development timeline • Conduct observational study to assess the effect of NPI, see e.g., ref. …”

“…• Use Model Informed Drug Development (MIDD) to leverage the totality of evidence for an optimal trial design and extrapolation 72 , 73…”

Themes in Clinical Pharmacology and Therapeutics

“…The advantage of this format is that it gives the journal more flexibility to respond to emerging topics in an agile and timely manner. We published 4 such mini‐themes in 2021 9–12 ( Table 1 ) and are planning to build on their success and positive reception by our readers and authors in the coming years.…”