A Physiologically-Based Pharmacokinetic (PBPK) Model Network for the Prediction of CYP1A2 and CYP2C19 Drug-Drug-Gene Interactions with Fluvoxamine, Omeprazole, S-mephenytoin, Moclobemide, Tizanidine, Mexiletine, Ethinylestradiol, and Caffeine.

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug-drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration-time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.

CYP3A4-mediated effects of rifampicin on the pharmacokinetics of vilaprisan and its UGT1A1-mediated effects on bilirubin glucuronidation in humans.

AIMS: The primary aim of the present study was to quantify the effects of rifampicin, a strong cytochrome P450 (CYP) 3A4 inducer, on the pharmacokinetics of the new selective progesterone receptor modulator, vilaprisan. In addition, the effects of rifampicin on the glucuronidation of bilirubin, an endogenous UDP-glucuronosyltransferase family 1 member A1 (UGT1A1) substrate, were explored. METHODS: This was an open-label, two-period study in 12 healthy postmenopausal women. Subjects received a single oral dose of vilaprisan 4 mg in each period. In period 2, administration of vilaprisan was preceded and followed by rifampicin 600 mg day-1 . A subtherapeutic dose of midazolam (1 mg) was coadministered with vilaprisan to monitor CYP3A4 induction. Details of the administration and sampling schedule were optimized by means of a physiologically based pharmacokinetic model. Plasma concentrations of vilaprisan, midazolam, and 1'- hydroxy-midazolam were measured and rifampicin-associated changes in the glucuronidation of bilirubin were determined. RESULTS: As predicted by our model, the coadministration of rifampicin was associated with a substantial decrease in exposure to vilaprisan and midazolam - indicated by the following point estimates (90% confidence intervals) for the area under the plasma concentration-time curve from zero to the time of the last quantifiable concentration ratio with or without rifampicin: 0.040 (0.0325, 0.0505) for vilaprisan and 0.144 (0.117, 0.178) for midazolam. Further, it was associated with an increase in bilirubin glucuronidation, indicating that UGT1A1 was induced. CONCLUSIONS: The exposure to vilaprisan was reduced by 96%. Such a reduction is likely to render the drug therapeutically ineffective. Therefore, it is recommended that the use of strong CYP3A4 inducers is avoided when taking vilaprisan.

Clarithromycin, Midazolam, and Digoxin: Application of PBPK Modeling to Gain New Insights into Drug-Drug Interactions and Co-medication Regimens.

Clarithromycin is a substrate and mechanism-based inhibitor of cytochrome P450 (CYP) 3A4 as well as a substrate and competitive inhibitor of P-glycoprotein (P-gp) and organic anion-transporting polypeptides (OATP) 1B1 and 1B3. Administered concomitantly, clarithromycin causes drug-drug interactions (DDI) with the victim drugs midazolam (CYP3A4 substrate) and digoxin (P-gp substrate). The objective of the presented study was to build a physiologically based pharmacokinetic (PBPK) DDI model for clarithromycin, midazolam, and digoxin and to exemplify dosing adjustments under clarithromycin co-treatment. The PBPK model development included an extensive literature search for representative PK studies and for compound characteristics of clarithromycin, midazolam, and digoxin. Published concentration-time profiles were used for model development (training dataset), and published and unpublished individual profiles were used for model evaluation (evaluation dataset). The developed single-compound PBPK models were linked for DDI predictions. The full clarithromycin DDI model successfully predicted the metabolic (midazolam) and transporter (digoxin) DDI, the acceptance criterion (0.5 ≤ AUCratio,predicted/AUCratio,observed ≤ 2) was met by all predictions. During co-treatment with 250 or 500 mg clarithromycin (bid), the midazolam and digoxin doses should be reduced by 74 to 88% and by 21 to 22%, respectively, to ensure constant midazolam and digoxin exposures (AUC). With these models, we provide highly mechanistic tools to help researchers understand and characterize the DDI potential of new molecular entities and inform the design of DDI studies with potential CYP3A4 and P-gp substrates.

A systematic evaluation of the use of physiologically based pharmacokinetic modeling for cross-species extrapolation.

Transfer of knowledge along the different phases of drug development is a fundamental process in pharmaceutical research. In particular, cross-species extrapolation between different laboratory animals and further on to first-in-human trials is challenging because of the uncertain comparability of physiological processes. Physiologically based pharmacokinetic (PBPK) modeling allows translation of mechanistic knowledge from one species to another by specifically considering physiological and biochemical differences in between. We here evaluated different knowledge-driven approaches for cross-species extrapolation by systematically incorporating specific model parameter domains of a target species into the PBPK model of a reference species. Altogether, 15 knowledge-driven approaches were applied to murine and human PBPK models of 10 exemplary drugs resulting in 300 different extrapolations. Statistical analysis of the quality of the different extrapolations revealed not only species-specific physiology as the key determinant in cross-species extrapolation but also identified a synergistic effect when considering both kinetic rate constants and gene expression profiles of relevant enzymes and transporters. Moreover, we show that considering species-specific physiology, plasma protein binding, enzyme and transport kinetics, as well as tissue-specific gene expression profiles in PBPK modeling increases accuracy of cross-species extrapolations and thus supports first-in-human trials based on prior preclinical knowledge.

A defined subset of adenylyl cyclases is regulated by bicarbonate ion.

The molecular basis by which organisms detect and respond to fluctuations in inorganic carbon is not known. The cyaB1 gene of the cyanobacterium Anabaena sp. PCC7120 codes for a multidomain protein with a C-terminal class III adenylyl cyclase catalyst that was specifically stimulated by bicarbonate ion (EC50 9.6 mm). Bicarbonate lowered substrate affinity but increased reaction velocity. A point mutation in the active site (Lys-646) reduced activity by 95% and was refractory to bicarbonate activation. We propose that Lys-646 specifically coordinates bicarbonate in the active site in conjunction with an aspartate to threonine polymorphism (Thr-721) conserved in class III adenylyl cyclases from diverse eukaryotes and prokaryotes. Using recombinant proteins we demonstrated that adenylyl cyclases that contain the active site threonine (cyaB of Stigmatella aurantiaca and Rv1319c of Mycobacterium tuberculosis) are bicarbonate-responsive, whereas adenylyl cyclases with a corresponding aspartate (Rv1264 of Mycobacterium) are bicarbonate-insensitive. Large numbers of class III adenylyl cyclases may therefore be activated by bicarbonate. This represents a novel mechanism by which diverse organisms can detect bicarbonate ion.

A GAF-domain-regulated adenylyl cyclase from Anabaena is a self-activating cAMP switch.

The gene cyaB1 from the cyanobacterium Anabaena sp. PCC 7120 codes for a protein consisting of two N-terminal GAF domains (GAF-A and GAF-B), a PAS domain and a class III adenylyl cyclase catalytic domain. The catalytic domain is active as a homodimer, as demonstrated by reconstitution from complementary inactive point mutants. The specific activity of the holoenyzme increased exponentially with time because the product cAMP activated dose dependently and nucleotide specifically (half-maximally at 1 microM), identifying cAMP as a novel GAF domain ligand. Using point mutants of either the GAF-A or GAF-B domain revealed that cAMP activated via the GAF-B domain. We replaced the cyanobacterial GAF domain ensemble in cyaB1 with the tandem GAF-A/GAF-B assemblage from the rat cGMP-stimulated phosphodiesterase type 2, and converted cyaB1 to a cGMP-stimulated adenylyl cyclase. This demonstrated the functional conservation of the GAF domain ensemble since the divergence of bacterial and eukaryotic lineages >2 billion years ago. In cyanobacteria, cyaB1 may act as a cAMP switch to stabilize committed developmental decisions.