This document was developed to enable greater consistency in the practice, application, and documentation of Model‐Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of “good practice” recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.

GW274150 at doses predicted to inhibit iNOS >80% did not differ from placebo in the prophylaxis of migraine. The results do not support a role of iNOS inhibition in migraine prevention.

Domagrozumab, a monoclonal antibody that binds to myostatin, is being developed for Duchenne muscular dystrophy (DMD) boys following a first-in-human study in healthy adults. Literature reporting pharmacokinetic parameters of monoclonal antibodies suggested that body-weight- and body-surface-area-adjusted clearance and volume of distribution estimates between adults and children are similar for subjects older than 6 years. Population modeling identified a Michaelis-Menten binding kinetics model to optimally characterize the target mediated drug disposition profile of domagrozumab and identified body mass index on the volume of distribution as the only significant covariate. Model parameters were predicted with high-precision pharmacokinetics (clearance 1.01 × 10 L/[h·kg]; central volume of distribution 457 × 10 L/kg; maximum elimination rate 17.5 × 10 nmol/[h·kg], Km 10.6 nmol/L) and pharmacodynamics (myostatin turnover rate 457 × 10 h ; complex removal rate 90 × 10 h ; half-saturation constant 4.32 nmol/L) and were used to predict target coverage for dosage selection in the DMD population. Additionally, allometric approaches (estimated scaling exponents (standard error) for clearance and volume were 0.81 [0.01] and 0.98 [0.02], respectively) in conjunction with a separate analysis to obtain the population mean weight and standard deviation suggested that if dosed per body weight, an only 11% difference in clearance is expected between the heaviest and lightest patient, thus preventing the need for dose adjustment. In summary, quantitative approaches were instrumental in bridging and derisking the fast-track development of domagrozumab in DMD.

This study investigated the disposition of coagulation factor VIII activity in 754 patients with moderate to severe hemophilia A following the administration of moroctocog alfa, a B-domain deleted recombinant factor VIII. Data analyzed included patients aged 1 day to 73 years enrolled in 13 studies conducted over a period of 20 years in 25 countries. A two-compartment population pharmacokinetic model with a baseline model described the pooled data well. Body size, age, inhibitors, race, and analytical assay were identified as significant predictors of factor VIII disposition. In addition, simulations of prophylactic dosing schedules in several pediatric cohorts showed large variability and suggest that younger patients would require higher weight-adjusted doses than adolescents to achieve target factor VIII trough activity when receiving every other day or twice weekly dosing.

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

Model-based drug development (MBDD) is accepted as a vital approach in understanding patients' drug-related benefit and risk by integrating quantitative information integration from diverse sources collected throughout drug development.1 This perspective introduces the activities of the Drug and Disease Model Resources (DDMoRe) consortium, founded in 2011 through the Innovative Medicines Initiative Joint Undertaking (IMI-JU)2 as a European public–private partnership to address a lack of common tools, languages, and standards for modeling and simulation (M&S) to improve model-based knowledge integration.

The registration and approval of novel medicines have traditionally been based on evidence arising from large prospective trials. Such an approach is often not possible or unsuitable to evaluate the benefit-risk balance in special populations (e.g., children, ethnic groups, rare diseases). Inferences by modeling and simulation can play a major role in evidence synthesis. A framework is proposed that promotes its acceptability and the basis for decision making during development, registration, and therapeutic use of drugs.

In pulmonary hypertension, as in many other diseases, there is a need for a smarter approach to evaluating new treatments. The traditional randomized controlled trial has served medical science well, but constrains the development of treatments for rare diseases. A workshop was established to consider alternative clinical trial designs in pulmonary hypertension and here discusses their merits, limitations and challenges to implementation of novel approaches.