No abstract

Using physiologically based pharmacokinetic modeling, we predicted the magnitude of drug-drug interactions (DDIs) for studies with rifampicin and seven CYP3A4 probe substrates administered i.v. (10 studies) or orally (19 studies). The results showed a tendency to underpredict the DDI magnitude when the victim drug was administered orally. Possible sources of inaccuracy were investigated systematically to determine the most appropriate model refinement. When the maximal fold induction (Indmax) for rifampicin was increased (from 8 to 16) in both the liver and the gut, or when the Indmax was increased in the gut but not in liver, there was a decrease in bias and increased precision compared with the base model (Indmax = 8) [geometric mean fold error (GMFE) 2.12 vs. 1.48 and 1.77, respectively]. Induction parameters (mRNA and activity), determined for rifampicin, carbamazepine, phenytoin, and phenobarbital in hepatocytes from four donors, were then used to evaluate use of the refined rifampicin model for calibration. Calibration of mRNA and activity data for other inducers using the refined rifampicin model led to more accurate DDI predictions compared with the initial model (activity GMFE 1.49 vs. 1.68; mRNA GMFE 1.35 vs. 1.46), suggesting that robust in vivo reference values can be used to overcome interdonor and laboratory-to-laboratory variability. Use of uncalibrated data also performed well (GMFE 1.39 and 1.44 for activity and mRNA). As a result of experimental variability (i.e., in donors and protocols), it is prudent to fully characterize in vitro induction with prototypical inducers to give an understanding of how that particular system extrapolates to the in vivo situation when using an uncalibrated approach.

Prospective simulations of human pharmacokinetic (PK) parameters and plasma concentration-time curves using in vitro in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models are becoming a vital part of the drug discovery and development process. This paper presents a strategy to deliver prospective simulations in support of clinical candidate nomination. A three stage approach of input parameter evaluation, model selection and multiple scenario simulation is utilized to predict the key components influencing human PK; absorption, distribution and clearance. The Simcyp W simulator is used to illustrate the approach and four compounds are presented as case studies. In general, the prospective predictions captured the observed clinical data well. Predicted C max was within 2-fold of observed data for all compounds and AUC was within 2-fold for all compounds following a single dose and three out of four compounds following multiple doses. Similarly, t max was within 2-fold of observed data for all compounds. However, C last was less accurately captured with two of the four compounds predicting C last within 2-fold of observed data following a single dose. The trend in results was towards overestimation of AUC and C last , this was particularly apparent for compound 2 for which clearance was likely underestimated via IVIVE. The prospective approach to simulating human PK using IVIVE and PBPK modeling outlined here attempts to utilize all available in silico, in vitro and in vivo preclinical data in order to determine the most appropriate assumptions to use in prospective predictions of absorption, distribution and clearance to aid clinical candidate nomination.

PI3Kδ is a lipid kinase and a member of a larger family of enzymes, PI3K class IA(α, β, δ) and IB (γ), which catalyze the phosphorylation of PIP2 to PIP3. PI3Kδ is mainly expressed in leukocytes, where it plays a critical, nonredundant role in B cell receptor mediated signaling and provides an attractive opportunity to treat diseases where B cell activity is essential, e.g., rheumatoid arthritis. We report the discovery of novel, potent, and selective PI3Kδ inhibitors and describe a structural hypothesis for isoform (α, β, γ) selectivity gained from interactions in the affinity pocket. The critical component of our initial pharmacophore for isoform selectivity was strongly associated with CYP3A4 time-dependent inhibition (TDI). We describe a variety of strategies and methods for monitoring and attenuating TDI. Ultimately, a structure-based design approach was employed to identify a suitable structural replacement for further optimization.

This work is the first demonstration of the use of PBPK modelling to predict MMAE-based DDI potential. The described model can be extended to assess the DDI potential of other vc-MMAE ADCs.