A model‐based meta‐analysis was performed with reported data from obese subjects and patients with type 2 diabetes (T2DM) to characterize the effects of dipeptidyl peptidase 4 (DPP4) inhibitors, gastric inhibitory polypeptides (GIPs), glucagon‐like peptide‐1 (GLP1), and dual GIP/GLP1 agonists, or a combination of these antidiabetic drugs (ADs) on heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A systematic literature search and review after the Cochrane method identified sources for investigational and approved ADs resulted in a comprehensive database with data from 178 clinical studies in obese subjects and patients with T2DM. Results indicated that there were AD class‐dependent effects on HR and SBP, whereas no clear AD‐related effects on DBP were found. All AD classes, except for DPP4 inhibitors, increased HR. The largest increase of 12 bpm was seen with GLP1 receptor agonists. All AD classes appeared to decrease SBP. DPP4 inhibitors were associated with a marginal decrease of ~ 1 mmHg, whereas GLP1 and GIP/GLP1 dual agonists exhibited the largest decrease of ~ 3 mmHg in SBP. AD‐related effects were similar in obese subjects and patients with T2DM. In conclusion, there are clinically relevant AD‐related effects on both HR and SBP, but not on DBP. DPP4 inhibitors are associated with the smallest (if at all) effects on HR and SBP, whereas GLP1 inhibitors exhibited the largest effects on these two cardiovascular end points. Additional studies are warranted to further investigate how AD‐related SBP decreases combined with HR increases affect long‐term cardiovascular mortality.

Ibrutinib (IBRU) is an oral Bruton's tyrosine kinase (BTK) inhibitor, approved by US FDA for the treatment of chronic lymphocytic leukemia (CLL/SLL) and mantle cell lymphoma (MCL) patients having received at least one prior therapy. A nonlinear mixed-effects population model was developed to describe the PK of IBRU in patients with B-Cell malignancies and to establish the effect of pathophysiological covariates on its PK behavior. The relationship between PK and BTK engagement in peripheral blood mononuclear cells (PBMC) was also explored. IBRU PK data (3477 observations in 245 patients) were available in patients with MCL, CLL/SLL and recurrent B-cell malignancies at dose levels from 1.25 to 12.5 mg/kg and at fixed doses from 420 to 840 mg once daily. An additional phase 2 study in 119 patients with MCL (772 observations) treated at 560 mg once daily was used to validate the PK model. BTK occupancy was assessed (694 observations in 127 patients) in PBMCs using a fluorescent affinity probe. Various models were tested on the data using the first-order conditional estimation method as implemented in NONMEM version 7.1. A 2-compartment linear model with sequential zero-first order absorption and first order elimination was able to accommodate available PK data, including those of the validation dataset (prediction errors <15%). PK was dose- and time- independent. IBRU was rapidly absorbed, extensively distributed (volume of distribution at steady-state ~ 10,000 L) and cleared (apparent oral clearance ~1000 L/h). Relative bioavailability in the fasting state was about one third lower compared to the fed condition used in the clinical trials. No significant effect of other pathophysiological covariates on the PK was found (including sex, age or indication) except for body weight and coadministration of antacids, which had a marginal effect on the volume of distribution and duration of absorption, respectively. Analysis of PK-BTK engagement suggested that IBRU is a potent inhibitor of the BTK activity and that its interaction with BTK is rapid and durable. Citation Format: Italo Poggesi, Maria Luisa Sardu, Eleonora Marostica, Juthamas Sukbuntherng, Betty Y. Chang, Jan de Jong, Xavier Woot de Trixhe, An Vermeulen, Giuseppe De Nicolao, Susan Mary O'Brien, John C Byrd, Ranjana H Advani, Danelle Frances James, William Deraedt, Darrin Beaupre, Michael Wang. Population pharmacokinetic-pharmacodynamic (PKPD) modeling of ibrutinib in patients with B-cell malignancies. [abstract]. In: Proceedings of the AACR Special Conference on Hematologic Malignancies: Translating Discoveries to Novel Therapies; Sep 20-23, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(17 Suppl):Abstract nr B19.

The mathematical modeling of tumor xenograft experiments following the dosing of antitumor drugs has received much attention in the last decade. Biomarker data can further provide useful insights on the pathological processes and be used for translational purposes in the early clinical development. Therefore, it is of particular interest the development of integrated pharmacokinetic-pharmacodynamic (PK-PD) models encompassing drug, biomarker and tumor-size data. This paper investigates the reciprocal consistency of three types of models: drug-to-tumor, such as established drug-driven tumor growth inhibition (TGI) models, drug-to-biomarker, e.g. indirect response models, and biomarker-to-tumor, e.g. the more recent biomarker-driven TGI models. In particular, this paper derives a mathematical relationship that guarantees the steady-state equivalence of the cascade of drug-to-biomarker and biomarker-to-tumor models with a drug-to-tumor TGI model. Using the Simeoni TGI model as a reference, conditions for steady-state equivalence are worked out and used to derive a new biomarker-driven model. Simulated and real data are used to show that in realistic cases the steady-state equivalence extends also to transient responses. The possibility of predicting the drug-to-tumor potency of a new candidate drug based only on biomarker response is discussed.

This chapter provides a general overview of the role in drug development of the model based approaches adopted both in the decision making process and to meet the requirements for market authorization and approval. In particular, the complex scenarios that pharmaceutical industries have to face in order to launch new chemical entities are described. Modeling and simulation approaches provide a fundamental contribution in optimizing drug development processes. Models can be used to: (i) quantitatively evaluate the effects and the risk:benefit ratios for a new treatment; (ii) simulate the outcomes of experimentally untested conditions; and (iii) devise the best experimental design, and also providing a reasonable guess of the probability of technical success. In this way, the development of compounds with a low probability of being approved can be stopped, allowing the redirection of resources to projects with higher probabilities of success. Notably, a dedicated pharmacometric division was recently formed at the US Food and Drug Administration (FDA), as the agency considered that it was urgent and crucial to boost the integration of pharmacometric expertise. In this chapter, some achievements in the oncology therapeutic area are illustrated through five paradigmatic semi-mechanistic pharmacokinetic–pharmakodynamic models, covering all phases of drug development from preclinical to clinical. For each model, the discussion ranges from the relevant background and mathematical formulation to application and impact.