Autologous Chimeric antigen receptor (CAR-T) cell therapy has been highly successful in the treatment of aggressive hematological malignancies and is also being evaluated for the treatment of solid tumors as well as other therapeutic areas. A challenge, however, is that up to 60% of patients do not sustain a long-term response. Low CAR-T cell exposure has been suggested as an underlying factor for a poor prognosis. CAR-T cell therapy is a novel therapeutic modality with unique kinetic and dynamic properties. Importantly, "clear" dose-exposure relationships do not seem to exist for any of the currently approved CAR-T cell products. In other words, dose increases have not led to a commensurate increase in the measurable in vivo frequency of transferred CAR-T cells. Therefore, alternative approaches beyond dose titration are needed to optimize CAR-T cell exposure. In this paper, we provide examples of actionable variables - design elements in CAR-T cell discovery, development, and clinical practice, which can be modified to optimize autologous CAR-T cell exposure. Most of these actionable variables can be assessed throughout the various stages of discovery and development as part of a well-informed research and development program. Model-informed drug development approaches can enable such study and program design choices from discovery through to clinical practice and can be an important contributor to cell therapy effectiveness and efficiency.

Model-informed drug development (MIDD) utilizes the knowledge extracted from relevant data to improve the efficiency of decision making within the pharmaceutical industry. The MIDD framework creates overlap between the quantitative disciplines, including statistics and pharmacometrics, with many opportunities for collaboration. MIDD necessitates effective alignment in the thoughts and deeds of statisticians and pharmacometricians, which is not a sector norm. The challenge of greater collaboration must be met in order for MIDD to realize its potential.

Systems pharmacology integrates structural biological and pharmacological knowledge and experimental data, enabling dissection of organism and drug properties and providing excellent predictivity. The development of systems pharmacology models is a significant task requiring massive amounts of background information beyond individual trial data. The qualification of models needs repetitive demonstration of successful predictions. Open Systems Pharmacology is a community that develops, qualifies, and shares professional open source software tools and models in a collaborative open-science way.

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.

To assess the link between early tumor shrinkage (ETS) and progression-free survival (PFS) based on historical first-line metastatic renal cell carcinoma (mRCC) data.

BACKGROUND:A model-based meta-analysis (MBMA) is a type of meta-regression that uses nonlinear mixed-effects models estimated on trial-level data to relate patient and trial characteristics, dosing, biomarkers, and outcomes of treatment. OBJECTIVES:To use a pharmacometric MBMA within a pharmacoeconomic model of chronic obstructive pulmonary disease (COPD). METHODS:A Markov microsimulation model was developed to estimate monthly changes in the key disease severity metrics of COPD (forced expiratory volume in 1 second [FEV1] and exacerbations) to compare a hypothetical drug that increases FEV1 to usual care. The MBMA was used to predict a baseline exacerbation rate in a group of actual trial patients, given their known baseline FEV1. The hypothetical drug increased FEV1, thereby decreasing individuals' predicted exacerbation rates. Individual patient simulations allowed stochastic changes in monthly FEV1 decline. RESULTS:In a sample of 1097 trial patients with a mean FEV1 of 50%, the MBMA predicted 0.93 exacerbations per year on average. The exacerbation rate ranged from 0.52 to 1.3 per year across moderate and severe patient subgroups. A hypothetical anti-inflammatory drug that increased FEV1 by 50 ml decreased exacerbations by 26%. Given a simplified estimation of costs and quality-adjusted life-years (QALYs) associated with COPD, a drug with a 50-ml increase priced at €35/mo had an incremental cost-effectiveness ratio ranging from €13,000/QALY to approximately €207,000/QALY across patient severity subgroups. CONCLUSIONS:The synergistic aspects of MBMA and pharmacoeconomic modeling are highlighted in this hypothetical example. Markov microsimulation modeling allows the finer predictions of MBMA to inform parameters. Such an approach has utility in both early-phase cost-effectiveness estimations and trial design.

PURPOSE:To assess the link between tumor growth inhibition (TGI) and overall survival (OS) based on historical renal cell carcinoma (RCC) data. To illustrate how simulations can help to identify TGI thresholds based on target OS benefit [i.e., hazard ratio (HR) compared with standard of care] to support new drug development in RCC.METHODS:Tumor size (TS) data were modeled from 2552 patients with first-line or refractory RCC who received temsirolimus, interferon, sunitinib, sorafenib or axitinib in 10 Phase II or Phase III studies. Three model-based TGI metrics estimates [early tumor shrinkage (ETS) at week 8, 10 or 12, time to tumor growth (TTG) and growth rate] as well as baseline prognostic factors were tested in multivariate lognormal models of OS. Model performance was evaluated by posterior predictive check of the OS distributions and hazard ratio across treatments.RESULTS:TTG was the best TGI metric to predict OS. However, week 8 ETS had a satisfactory performance and was employed in order to maximize clinical utilization. The week 8 ETS to OS model was then used to simulate clinically relevant ETS thresholds for future Phase II studies with investigational treatments.CONCLUSIONS:The published OS model and resultant simulations can be leveraged to support Phase II design and predict expected OS and HR (based on early observed TGI data obtained in Phase II or Phase III studies), thereby informing important mRCC development decisions, e.g., Go/No Go and dose regimen selection.

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.

Predicting Reductions in Chronic Obstructive Pulmonary Disease (COPD) Exacerbations from FEV1-A Model-Based Meta-Analysis of Literature Data from Controlled Randomized Clinical Trials

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 Syst. Pharmacol. (2013) 2, e85; doi:10.1038/psp.2013.62; published online 4 December 2013

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.

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 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.

New approaches quantifying the effect of treatment are needed in oncology to improve the drug development process and to enable treatment optimization for existing therapies. This thesis focuses on the development of pharmacometric models for biomarkers, side effects and efficacy in order to identify predictors of clinical response in anti-cancer drug therapy. The variability in myelosuppression was characterized in six different cytotoxic anticancer treatments to evaluate a model-based dose individualization approach utilizing neutrophil counts as a biomarker. The estimated impact of inter-occasion variability was relatively low in relation to the inter-individual variability, indicating that myelosuppression is predictable from one treatment course to another. The approach may thereby be useful for dose optimization within an individual. To further study and to identify predictors for the severe side effect febrile neutropenia (FN), the relationship between the shape of the myelosuppression time-course and the probability of FN was characterized. Patients with a rapid decline in neutrophil counts and high drug sensitivity were identified to have a higher probability of developing FN compared with other patients who experience grade 4 neutropenia. Predictors of clinical response in patients receiving sunitinib for the treatment of gastro-intestinal stromal tumor (GIST) were identified by the development of an integrated modeling framework. Drug exposure, biomarkers, tumor dynamics, side effects and overall survival (OS) were linked in a unified structure, and univariate and multivariate exposure variables were tested for their predictive capacities. The soluble biomarker, sVEGFR-3 and tumor size at start of treatment were found to be promising predictors of overall survival, with decreased sVEGFR-3 levels and smaller baseline tumor size being predictive of longer OS. Also hypertension and neutropenia was identified as predictors of OS. The developed modeling framework may be useful to monitor clinical response, optimize dosing in sunitinib and to facilitate dose individualization.

Diabet. Med. 27, 1217–1225 (2010)AbstractObjective To examine the impact of telephone follow‐up interventions on glycaemic control in patients with Type 2 diabetes.Methods This was a systematic review and meta‐analysis of randomized controlled trials using The Cochrane Library, including the Cochrane central register of controlled trials; MEDLINE, EMBASE, PsycINFO and CINHAL, together with citation searching. The included studies were randomized controlled trials examining the effect of a telephone follow‐up intervention on glycaemic control in patients with Type 2 diabetes. All the included trials were subject to critical appraisal. Data were extracted on study design, characteristics of patients, exact nature of the telephone intervention and details of comparison. Pooled standardized effects were calculated for the primary outcome. Glycaemic control was measured by HbA1c.Results HbA1c levels reported in the reviewed studies were pooled using random effects models. The standardized effect of telephone follow‐up was equivocal, with endpoint data showing weighted mean differences of –0.44 (95% CI –0.93 to 0.06) (Z = –1.72, P = 0.08) in favour of the telephone follow‐up intervention. Subgroup analysis of more intensive interventions (interactive follow‐up with health professional plus automated follow‐up or non‐interactive follow‐up) showed (n = 1057) a significant benefit in favour of the treatment group, with a standardized mean difference of –0.84 (95% CI –1.67 to 0.0) (Z = 1.97, P = 0.05), indicating that more intensive (targeted) modes of follow‐up may have better effects on glycaemic control.Conclusions The analysis suggested that telephone follow‐up interventions following a more intensive targeted approach could have a positive impact on glycaemic control for Type 2 diabetes.

The pharmacokinetics of voriconazole in children receiving 4 mg/kg intravenously (i.v.) demonstrate substantially lower plasma exposures (as defined by area under the concentration-time curve [AUC]) than those in adults receiving the same therapeutic dosage. These differences in pharmacokinetics between children and adults limit accurate prediction of pediatric voriconazole exposure based on adult dosages. We therefore studied the pharmacokinetics and tolerability of higher dosages of an i.v.-to-oral regimen of voriconazole in immunocompromised children aged 2 to <12 years in two dosage cohorts for the prevention of invasive fungal infections. The first cohort received 4 mg/kg i.v. every 12 h (q12h), then 6 mg/kg i.v. q12h, and then 4 mg/kg orally (p.o.) q12h; the second received 6 mg/kg i.v. q12h, then 8 mg/kg i.v. q12h, and then 6 mg/kg p.o. q12h. The mean values for the AUC over the dosing interval (AUCτ) for 4 mg/kg and 6 mg/kg i.v. in cohort 1 were 11,827 and 22,914 ng.h/ml, respectively, whereas the mean AUCτ values for 6 mg/kg and 8 mg/kg i.v. in cohort 2 were 17,249 and 29,776 ng.h/ml, respectively. High interpatient variability was observed. The bioavailability of the oral formulation in children was approximately 65%. The safety profiles were similar in the two cohorts and age groups. The most common treatment-related adverse event was increased gamma glutamyl transpeptidase levels. There was no correlation between adverse events and voriconazole exposure. In summary, voriconazole was tolerated to a similar degree regardless of dosage and age; the mean plasma AUCτ for 8 mg/kg i.v. in children approached that for 4 mg/kg i.v. in adults, thus representing a rationally selected dosage for the pediatric population.

Voriconazole is a potent triazole with broad-spectrum antifungal activity against clinically significant and emerging pathogens. The present population pharmacokinetic analysis evaluated voriconazole plasma concentration-time data from three studies of pediatric patients of 2 to <12 years of age, incorporating a range of single or multiple intravenous (i.v.) and/or oral (p.o.) doses. An appropriate pharmacokinetic model for this patient population was created using the nonlinear mixed-effect modeling approach. The final model described voriconazole elimination by a Michaelis-Menten process and distribution by a two-compartment model. It also incorporated a statistically significant (P < 0.001) influence of the CYP2C19 genotype and of the alanine aminotransferase level on clearance. The model was used in a number of deterministic simulations (based on various fixed, mg/kg of body weight, and individually adjusted doses) aimed at finding suitable i.v. and p.o. voriconazole dosing regimens for pediatric patients. As a result, 7 mg/kg twice a day (BID) i.v. or 200 mg BID p.o., irrespective of body weight, was recommended for this patient population. At these doses, the pediatric area-under-the-curve (AUC) distribution exhibited the least overall difference from the adult AUC distribution (at dose levels used in clinical practice). Loading doses or individual dosage adjustments according to baseline covariates are not considered necessary in administering voriconazole to children.