Development of a Physiologically Based Pharmacokinetic Population Model for Diabetic Patients and its Application to Understand Disease-drug-drug Interactions.
Clinical pharmacokinetics
INTRODUCTION:The activity changes of cytochrome P450 (CYP450) enzymes, along with the complicated medication scenarios in diabetes mellitus (DM) patients, result in the unanticipated pharmacokinetics (PK), pharmacodynamics (PD), and drug-drug interactions (DDIs). Physiologically based pharmacokinetic (PBPK) modeling has been a useful tool for assessing the influence of disease status on CYP enzymes and the resulting DDIs. This work aims to develop a novel diabetic PBPK population model to facilitate the prediction of PK and DDI in DM patients. METHODS:First, mathematical functions were constructed to describe the demographic and non-CYP physiological characteristics specific to DM, which were then incorporated into the PBPK model to quantify the net changes in CYP enzyme activities by comparing the PK of CYP probe drugs in DM versus non-DM subjects. RESULTS:The results show that the enzyme activity is reduced by 32.3% for CYP3A4/5, 39.1% for CYP2C19, and 27% for CYP2B6, while CYP2C9 activity is enhanced by 38% under DM condition. Finally, the diabetic PBPK model was developed through integrating the DM-specific CYP activities and other parameters and was further used to perform PK simulations under 12 drug combination scenarios, among which 3 combinations were predicted to result in significant PK changes in DM, which may cause DDI risks in DM patients. CONCLUSIONS:The PBPK modeling applied herein provides a quantitative tool to assess the impact of disease factors on relevant enzyme pathways and potential disease-drug-drug-interactions (DDDIs), which may be useful for dosing regimen optimization and minimizing the DDI risks associated with the treatment of DM.
10.1007/s40262-024-01383-2
Physiologically-based pharmacokinetic pharmacodynamic parent-metabolite model of edoxaban to predict drug-drug-disease interactions: M4 contribution.
CPT: pharmacometrics & systems pharmacology
This study aimed to develop a physiologically-based pharmacokinetic pharmacodynamic (PBPK/PD) parent-metabolite model of edoxaban, an oral anticoagulant with a narrow therapeutic index, and to predict pharmacokinetic (PK)/PD profiles and potential drug-drug-disease interactions (DDDIs) in patients with renal impairment. A whole-body PBPK model with a linear additive PD model of edoxaban and its active metabolite M4 was developed and validated in SimCYP for healthy adults with or without interacting drugs. The model was extrapolated to situations including renal impairment and drug-drug interactions (DDIs). Observed PK and PD data in adults were compared with predicted data. The effect of several model parameters on the PK/PD response of edoxaban and M4 was investigated in sensitivity analysis. The PBPK/PD model successfully predicted PK profiles of edoxaban and M4 as well as anticoagulation PD responses with or without the influence of interacting drugs. For patients with renal impairment, the PBPK model successfully predicted the fold change in each impairment group. Inhibitory DDI and renal impairment had a synergistic effect on the increased exposure of edoxaban and M4, and their downstream anticoagulation PD effect. Sensitivity analysis and DDDI simulation show that renal clearance, intestinal P-glycoprotein activity, and hepatic OATP1B1 activity are the major factors affecting edoxaban-M4 PK profiles and PD responses. Anticoagulation effect induced by M4 cannot be ignored when OATP1B1 is inhibited or downregulated. Our study provides a reasonable approach to adjust the dose of edoxaban in several complicated scenarios especially when M4 cannot be ignored due to decreased OATP1B1 activity.
10.1002/psp4.12977
Application of a physiologically based pharmacokinetic model of rivaroxaban to prospective simulations of drug-drug-disease interactions with protein kinase inhibitors in cancer-associated venous thromboembolism.
British journal of clinical pharmacology
AIMS:Rivaroxaban is a viable anticoagulant for the management of cancer-associated venous thromboembolism (CA-VTE). A previously verified physiologically-based pharmacokinetic (PBPK) model of rivaroxaban established how its multiple pathways of elimination via both CYP3A4/2J2-mediated hepatic metabolism and organic anion transporter 3 (OAT3)/P-glycoprotein-mediated renal secretion predisposes rivaroxaban to drug-drug-disease interactions (DDDIs) with clinically relevant protein kinase inhibitors (PKIs). We proposed the application of PBPK modelling to prospectively interrogate clinically significant DDIs between rivaroxaban and PKIs (erlotinib and nilotinib) for dose adjustments in CA-VTE. METHODS:The inhibitory potencies of the PKIs on CYP3A4/2J2-mediated metabolism of rivaroxaban were characterized. Using prototypical OAT3 inhibitor ketoconazole, in vitro OAT3 inhibition assays were optimized to ascertain the in vivo relevance of derived transport inhibitory constants (K ). Untested DDDIs between rivaroxaban and erlotinib or nilotinib were simulated. RESULTS:Mechanism-based inactivation (MBI) of CYP3A4-mediated rivaroxaban metabolism by both PKIs and MBI of CYP2J2 by erlotinib were established. The importance of substrate specificity and nonspecific binding to derive OAT3-inhibitory K values of ketoconazole and nilotinib for the accurate prediction of interactions was illustrated. When simulated rivaroxaban exposure variations with concomitant erlotinib and nilotinib therapy were evaluated using published dose-exposure equivalence metrics and bleeding risk analyses, dose reductions from 20 to 15 and 10 mg in normal and mild renal dysfunction, respectively, were warranted. CONCLUSION:We established a PBPK-DDDI model to prospectively evaluate clinically relevant interactions between rivaroxaban and PKIs for the safe and efficacious management of CA-VTE.
10.1111/bcp.15158
Assessment of Vedolizumab Disease-Drug-Drug Interaction Potential in Patients With Inflammatory Bowel Diseases.
Clinical pharmacology in drug development
Disease-drug-drug interactions (DDDIs) have been identified in some inflammatory diseases in which elevated proinflammatory cytokines can downregulate the expression of cytochrome P450 (CYP) enzymes, potentially increasing systemic exposure to drugs metabolized by CYPs. Following anti-inflammatory treatments, CYP expression may return to normal, resulting in reduced drug exposure and diminished clinical efficacy. Vedolizumab has a well-established positive benefit-risk profile in patients with ulcerative colitis (UC) or Crohn's disease (CD) and has no known systemic immunosuppressive activity. A stepwise assessment was conducted to evaluate the DDDI potential of vedolizumab to impact exposure to drugs metabolized by CYP3A through cytokine modulation. First, a review of published data revealed that patients with UC or CD have elevated cytokine concentrations relative to healthy subjects; however, these concentrations remained below those reported to impact CYP expression. Exposure to drugs metabolized via CYP3A also appeared comparable between patients and healthy subjects. Second, serum samples from patients with UC or CD who received vedolizumab for 52 weeks were analyzed and compared with healthy subjects. Cytokine concentrations and the 4β-hydroxycholesterol-to-cholesterol ratio, an endogenous CYP3A4 biomarker, were comparable between healthy subjects and patients both before and during vedolizumab treatment. Finally, a medical review of postmarketing DDDI cases related to vedolizumab from the past 6 years was conducted and did not show evidence of any true DDDIs. Our study demonstrated the lack of clinically meaningful effects of disease or vedolizumab treatment on the exposure to drugs metabolized via CYP3A through cytokine modulation in patients with UC or CD.
10.1002/cpdd.891
Model-Based Risk Prediction of Rivaroxaban with Amiodarone for Moderate Renal Impaired Elderly Population.
Cardiovascular drugs and therapy
PURPOSE:Increased bleeding risk was found associated with concurrent prescription of rivaroxaban and amiodarone. We previously recommended dose adjustment for rivaroxaban utilizing a physiologically based pharmacokinetic (PBPK) modeling approach. Our subsequent in vitro studies discovered the pivotal involvement of human renal organic anion transporter 3 (hOAT3) in the renal secretion of rivaroxaban and the inhibitory potency of amiodarone. This study aimed to redefine the disease-drug-drug interactions (DDDI) between rivaroxaban and amiodarone and update the potential risks. METHODS:Prospective simulations were conducted with updated PBPK models of rivaroxaban and amiodarone incorporating hOAT3-related parameters. RESULTS:Simulations to recapitulate previously explored DDDI in renal impairment showed a higher bleeding tendency in all simulation scenarios after integrating hOAT3-mediated clearance into PBPK models. Further sensitivity analysis revealed that both hOAT3 dysfunction and age could affect the extent of DDDI, and age was shown to have a more pivotal role on rivaroxaban in vivo exposure. When amiodarone was prescribed along with our recommended dose reduction of rivaroxaban to 10 mg in moderate renal impaired elderly people, it could result in persistently higher rivaroxaban peak concentrations at a steady state. To better manage the increased bleeding risk among such a vulnerable population, a dose reduction of rivaroxaban to 2.5 mg twice daily resulted in its acceptable in vivo exposure. CONCLUSION:Close monitoring of bleeding tendency is essential for elderly patients with moderate renal impairment receiving co-prescribed rivaroxaban and amiodarone. Further dose reduction is recommended for rivaroxaban to mitigate this specific DDDI risk.
10.1007/s10557-021-07266-z
Understanding Statin-Roxadustat Drug-Drug-Disease Interaction Using Physiologically-Based Pharmacokinetic Modeling.
Clinical pharmacology and therapeutics
A different drug-drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug-drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.
10.1002/cpt.2980
A systematic review on disease-drug-drug interactions with immunomodulating drugs: A critical appraisal of risk assessment and drug labelling.
British journal of clinical pharmacology
AIM:Use of immunomodulating therapeutics for immune-mediated inflammatory diseases may cause disease-drug-drug interactions (DDDIs) by reversing inflammation-driven alterations in the metabolic capacity of cytochrome P450 enzymes. European Medicine Agency (EMA) and US Food and Drug Administration (FDA) guidelines from 2007 recommend that the DDDI potential of therapeutic proteins should be assessed. This systematic analysis aimed to characterize the available DDDI trials with immunomodulatory drugs, experimental evidence for a DDDI risk and reported DDDI risk information in FDA/EMA approved drug labelling. METHOD:For this systematic review, the EMA list of European Public Assessment Reports of human medicine was used to select immunomodulating monoclonal antibodies (mAbs) and tyrosine kinase inhibitors (TKIs) marketed after 2007 at risk for a DDDI. Selected drugs were included in PubMed and Embase searches to extract reported interaction studies. The Summary of Product Characteristics (SPCs) and the United States Prescribing Information (USPIs) were subsequently used for analysis of DDDI risk descriptions. RESULTS:Clinical interaction studies to evaluate DDDI risks were performed for 12 of the 24 mAbs (50%) and for none of the TKIs. Four studies identified a DDDI risk, of which three were studies with interleukin-6 (IL-6) neutralizing mAbs. Based on (non)clinical data, a DDDI risk was reported in 32% of the SPCs and in 60% of the USPIs. The EMA/FDA documentation aligned with the DDDI risk potential in 35% of the 20 cases. CONCLUSION:This systematic review reinforces that the risk for DDDI by immunomodulating drugs is target- and disease-specific. Drug labelling information designates the greatest DDDI risk to mAbs that neutralize the effects of IL-6, Tumor Necrosis Factor alfa (TNF-α) and interleukin-1 bèta (IL-1β) in diseases with systemic inflammation.
10.1111/bcp.15372