Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation.

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.

Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development.

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.

PKPD Modeling of Predictors for Adverse Effects and Overall Survival in Sunitinib-Treated Patients With GIST.

A modeling framework relating exposure, biomarkers (vascular endothelial growth factor (VEGF), soluble vascular endothelial growth factor receptor (sVEGFR)-2, -3, soluble stem cell factor receptor (sKIT)), and tumor growth to overall survival (OS) was extended to include adverse effects (myelosuppression, hypertension, fatigue, and hand-foot syndrome (HFS)). Longitudinal pharmacokinetic-pharmacodynamic models of sunitinib were developed based on data from 303 patients with gastrointestinal stromal tumor. Myelosuppression was characterized by a semiphysiological model and hypertension with an indirect response model. Proportional odds models with a first-order Markov model described the incidence and severity of fatigue and HFS. Relative change in sVEGFR-3 was the most effective predictor of the occurrence and severity of myelosuppression, fatigue, and HFS. Hypertension was correlated best with sunitinib exposure. Baseline tumor size, time courses of neutropenia, and relative increase of diastolic blood pressure were identified as predictors of OS. The framework has potential to be used for early monitoring of adverse effects and clinical response, thereby facilitating dose individualization to maximize OS.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e85; doi:10.1038/psp.2013.62; advance online publication 4 December 2013.

PKPD Modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as Predictors of Tumor Dynamics and Overall Survival Following Sunitinib Treatment in GIST.

The predictive value of longitudinal biomarker data (vascular endothelial growth factor (VEGF), soluble VEGF receptor (sVEGFR)-2, sVEGFR-3, and soluble stem cell factor receptor (sKIT)) for tumor response and survival was assessed based on data from 303 patients with imatinib-resistant gastrointestinal stromal tumors (GIST) receiving sunitinib and/or placebo treatment. The longitudinal tumor size data were well characterized by a tumor growth inhibition model, which included, as significant descriptors of tumor size change, the model-predicted relative changes from baseline over time for sKIT (most significant) and sVEGFR-3, in addition to sunitinib exposure. Survival time was best described by a parametric time-to-event model with baseline tumor size and relative change in sVEGFR-3 over time as predictive factors. Based on the proposed modeling framework to link longitudinal biomarker data with overall survival using pharmacokinetic-pharmacodynamic models, sVEGFR-3 demonstrated the greatest predictive potential for overall survival following sunitinib treatment in GIST.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e84; doi:10.1038/psp.2013.61; advance online publication 20 November 2013.

The Role of Modeling and Simulation in Development and Registration of Medicinal Products: Output From the EFPIA/EMA Modeling and Simulation Workshop.

The European Medicines Agency (EMA) and the Federation of Pharmaceutical Industries and Associations (EFPIA) hosted a workshop on modeling and simulation (M&S).(1) Representatives from industry, academia, and regulatory agencies from Europe and beyond discussed the role of M&S in the development and registration of medicinal products within plenary and breakout sessions (BOS). This manuscript summarizes the plenary discussion (Table 1) focusing on the European perspective. Deliverables from each BOS are included in separate papers.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e31; doi:10.1038/psp.2013.7; advance online publication 27 February 2013.

Model-based drug development: a rational approach to efficiently accelerate drug development.

The pharmaceutical industry continues to face significant challenges. Very few compounds that enter development reach the marketplace, and the investment required for each success can surpass $1.8 billion. Despite attempts to improve efficiency and increase productivity, total investment continues to rise whereas the output of new medicines declines. With costs increasing exponentially through each development phase, it is failure in phase II and phase III that is most wasteful. In today's development paradigm, late-stage failure is principally a result of insufficient efficacy. This is manifested as either a failure to differentiate sufficiently from placebo (shown for both novel and precedented mechanisms) or a failure to demonstrate sufficient differentiation from existing compounds. Set in this context, this article will discuss the role model-based drug development (MBDD) approaches can and do play in accelerating and optimizing compound development strategies through a series of illustrative examples.

Model-based drug development.

The low productivity and escalating costs of drug development have been well documented over the past several years. Less than 10% of new compounds that enter clinical trials ultimately make it to the market, and many more fail in the preclinical stages of development. These challenges in the "critical path" of drug development are discussed in a 2004 publication by the US Food and Drug Administration. The document emphasizes new tools and various opportunities to improve drug development. One of the opportunities recommended is the application of "model-based drug development (MBDD)." This paper discusses what constitutes the key elements of MBDD and how these elements should fit together to inform drug development strategy and decision-making.

The consequences of H2 receptor antagonist--piroxicam coadministration in patients with joint disorders.

A randomised crossover study was performed in subjects with rheumatoid arthritis (or other arthropathies) to investigate if any alteration in the steady pharmacokinetics of the NSAID piroxicam (a drug which is extensively metabolised via cytochrome P450) or its major metabolites occurred as a result of coadministering either cimetidine or nizatidine. Twelve females and 2 males with mean age, weight, and albumin concentrations of 58 years, 61 kg, and 40 g.L-1 respectively, completed the study. Comparisons were made between the following parameters: plasma piroxicam AUCs [AUC0-24(P)], plasma 5-hydroxypiroxicam AUCs [AUC0-24(5-OHP)], the ratio of these i.e. AUC0-24(5-OHP):AUC0-24(p), the % piroxicam daily dose excreted in urine as 5-hydroxypiroxicam (before and after glucuronidase incubation); and the mean of the steady state trough piroxicam, and 5-hydroxypiroxicam concentrations (obtained during each study phase in addition to the wash-out period). A statistically significant difference as a result of initiating either cimetidine or nizatidine was obtained only for the ratio AUC0-23(5-OHP):AUC0-24(P). This was indicative of a weak potential to inhibit piroxicam hydroxylation. No clinically significant alteration in the steady state pharmacokinetics of piroxicam occurred in these subjects as a result of cimetidine or nizatidine coadministration. Consequently it is unlikely that any adverse events would arise from these combinations.

Determination of piroxicam and its major metabolites in the plasma, urine and bile of humans by high-performance liquid chromatography.

A simple and sensitive liquid chromatographic method with ultraviolet detection is described for the determination of the nonsteroidal anti-inflammatory drug piroxicam and its major metabolites in human plasma, urine and bile. Separation of these components occurs on a reversed phase C10CN column with a mobile phase consisting of acetonitrile-water-sodium dihydrogenphosphate solution. The detection limit of the assay was 50 ng/ml with intra- and inter-assay coefficients of variation for piroxicam of the order of 2 and 5%, respectively. The assay linearity was good (typically r = 0.9999). This method can be readily utilised for clinical pharmacokinetic and mass-balance studies.