A strategy for early-risk predictions of clinical drug-drug interactions involving the GastroPlusTM DDI module for time-dependent CYP inhibitors

Physiologically based pharmacokinetic modeling to assess the drug-drug interactions of anaprazole with clarithromycin and amoxicillin in patients undergoing eradication therapy of H. pylori infection

OBJECTIVE

This study aimed to assess the pharmacokinetic (PK) interactions of anaprazole, clarithromycin, and amoxicillin using physiologically based pharmacokinetic (PBPK) models.

METHODS

The PBPK models for anaprazole, clarithromycin, and amoxicillin were constructed using the GastroPlus™ software (Version 9.7) based on the physicochemical data and PK parameters obtained from literature, then were optimized and validated in healthy subjects to predict the plasma concentration-time profiles of these three drugs and assess the predictive performance of each model. According to the analysis of the properties of each drug, the developed and validated models were applied to evaluate potential drug-drug interactions (DDIs) of anaprazole, clarithromycin, and amoxicillin.

RESULTS

The developed PBPK models properly described the pharmacokinetics of anaprazole, clarithromycin, and amoxicillin well, and all predicted PK parameters (Cmax,ss, AUC0-τ,ss) ratios were within 2.0-fold of the observed values. Furthermore, the application of these models to predict the anaprazole-clarithromycin and anaprazole-amoxicillin DDIs demonstrates their good performance, with the predicted DDI Cmax,ss ratios and DDI AUC0-τ,ss ratios within 1.25-fold of the observed values, and all predicted DDI Cmax,ss, and AUC0-τ,ss ratios within 2.0-fold. The simulated results show no need to adjust the dosage when co-administered with anaprazole in patients undergoing eradication therapy of H. pylori infection since the dose remained in the therapeutic range.

CONCLUSION

The whole-body PBPK models of anaprazole, clarithromycin, and amoxicillin were built and qualified, which can predict DDIs that are mediated by gastric pH change and inhibition of metabolic enzymes, providing a mechanistic understanding of the DDIs observed in the clinic of clarithromycin, amoxicillin with anaprazole.

Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward

The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.

Evaluation of ADMET Predictor in Early Discovery Drug Metabolism and Pharmacokinetics Project Work

A dataset consisting of measured values for LogD, solubility, metabolic stability in human liver microsomes (HLMs), and Caco-2 permeability was used to evaluate the prediction models for lipophilicity (S+LogD), water solubility (S+Sw_pH), metabolic stability in HLM (CYP_HLM_Clint), intestinal permeability (S+Peff), and P-glycoprotein (P-gp) substrate identification (P-gp substrate) in the software ADMET Predictor (AP) from Simulations Plus. The dataset consisted of a total of 4,794 compounds, with at least data from metabolic stability determinations in HLM, from multiple discovery projects at Medivir. Our evaluation shows that the global AP models can be used for categorization of high and low values based on predicted results for metabolic stability in HLM and intestinal permeability, and to give good predictions of LogD (R2= 0.79), guiding the synthesis of new compounds and for prioritizing in vitro ADME experiments. The model seems to overpredict solubility for the Medivir compounds, however. We also used the in-house datasets to build local models for LogD, solubility, metabolic stability, and permeability by using artificial neural network (ANN) models in the optional Modeler module of AP. Predictions of the test sets were performed with both the global and the local models, and the R2 values for linear regression for predicted versus measured HLM in vitro intrinsic clearance (CLint) based on logarithmic data were 0.72 for the in-house model and 0.53 for the AP model. The improved predictions with the local models are likely explained both by the specific chemical space of the Medivir dataset and laboratory-specific assay conditions for parameters that require biologic assay systems. SIGNIFICANCE STATEMENT: AP is useful early in projects for predicting and categorizing LogD, metabolic stability, and permeability, to guide the synthesis of new compounds, and for prioritizing in vitro ADME experiments. The building of local in-house prediction models with the optional AP Modeler Module can yield improved prediction success since these models are built on data from the same experimental setup and can also be based on compounds with similar structures.

A novel hydrophilic interaction chromatography assay characterization of 4-pyridoxic acid, an emergent renal organic anion transporter 1/3 transporter biomarker

Aim: 4-pyridoxic acid (PDA) has been proposed as an endogenous biomarker for renal organic anion transporter 1/3 (OAT1/3) inhibition. Clinical data are needed to support the proposal. Materials & methods: A hydrophilic interaction chromatography (HILIC)-LC/MS/MS assay was developed and characterized to support clinical drug-drug interaction (DDI) studies. Results: A HILIC-LC/MS/MS assay was successfully developed. PDA was measured in two clinical DDI studies; one where no significant OAT1/3 inhibition was observed and a second where a known inhibitor of the transporter was dosed. In both clinical studies, PDA plasma concentrations correlate to OAT1/3 function. Conclusion: The analysis of study samples from two clinical DDI studies using a HILIC-LC/MS/MS assay contributes further evidence that PDA is an endogenous biomarker for OAT1/3 inhibition.

Cathepsin S acts via protease-activated receptor 2 to activate sensory neurons and induce itch-like behaviour

Chronic itch is a debilitating condition characterised by excessive scratching and is a symptom frequently reported in skin diseases such as atopic dermatitis. It has been proposed that release of the cysteine protease Cathepsin S (CatS) from skin keratinocytes or immune cells resident in or infiltrating the skin could act as a pruritogen in chronic itch conditions. CatS is known to activate protease-activated receptor 2 (PAR2). We therefore hypothesised that enzymatic activation of neuronally expressed PAR2 by CatS was responsible for activation of sensory neurons and transmission of itch signals. Intradermally-injected human recombinant (hr)-CatS or the PAR2 agonist, SLIGRL-NH2 behaved as pruritogens by causing scratching behaviour in mice. Hr-CatS-induced scratching behaviour was prevented by CatS inhibitors and PAR2 antagonists and reduced by 50% in TRPV1-/- mice compared with wild-type mice, whilst no significant reduction in scratching behaviour was observed in TRPA1-/- mice. Cultured dorsal root ganglion (DRG) cells showed an increase in [Ca2+]i following incubation with hr-CatS, and the percentage of neurons that responded to hr-CatS decreased in the presence of a PAR2 antagonist or in cultures of neurons from TRPV1-/- mice. Taken together, our results indicate CatS acts as a pruritogen via PAR2 activation in TRPV1-expressing sensory neurons.

Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models

The cytochrome P450s (CYPs) play a central role in the metabolism of various endogenous and exogenous compounds including drugs. CYPs are vulnerable to inhibition and induction which can lead to adverse drug reactions. Therefore, insights into the underlying mechanism of CYP450 inhibition and the estimation of overall CYP inhibitor properties might serve as valuable tools during the early phases of drug discovery. Herein, we present a large data set of inhibitors against five major metabolic CYPs (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) for the evaluation of important physicochemical properties and ligand efficiency metrics to define property trends across various activity levels (active, efficient and inactive). Decision tree models for CYP inhibition were developed with an accuracy >90% for both the training set and 10-folds cross validation. Overall, molecular weight (MW), hydrogen bond acceptors/donors (HBA/HBD) and lipophilicity (clogP/logPo/w) represent important physicochemical descriptors for CYP450 inhibitors. However, highly efficient CYP inhibitors show mean MW, HBA, HBD and logP values between 294.18–482.40,5.0–8.2,1–7.29 and 1.68–2.57, respectively. Our results might help in optimization of toxicological profiles associated with new chemical entities (NCEs), through a better understanding of inhibitor properties leading to CYP-mediated interactions.

Analysis of Clinical Drug-Drug Interaction Data To Predict Magnitudes of Uncharacterized Interactions between Antiretroviral Drugs and Comedications

Despite their high potential for drug-drug interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also being ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required. We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes, such as CYP3A, and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers, and inhibitors of CYP3A to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs. The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and comedications were successfully quantified, meaning 53%, 85%, and 98% of the predictions were within 1.25-fold (0.80 to 1.25), 1.5-fold (0.66 to 1.48), and 2-fold (0.66 to 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or, conversely, minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs. The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs, being particularly relevant in an HIV setting, given the treatment's complexity, high DDI risk, and limited guidance on the management of DDIs.

A multiplex HRMS assay for quantifying selected human plasma bile acids as candidate OATP biomarkers

Aim: Selected bile acids (BAs) in plasma have been proposed as endogenous probes for assessing drug–drug interactions involving hepatic drug transporters such as the organic anion-transporting polypeptides (OATP1B1 and OATP1B3). Materials & methods: Plasma extracts were analyzed for selected BAs using a triple TOF API6600 high-resolution mass spectrometer. Results: Glycodeoxycholic acid 3-sulfate, glycochenodeoxycholic acid 3-sulfate, glycodeoxycholic acid 3-O-β-glucuronide and glycochenodeoxycholic acid 3-O-β-glucuronide are presented as potential OATP1B1/3 biomarkers.Conclusion: Six BAs are quantified in human plasma using a multiplexed high-resolution mass spectrometry method. Glycodeoxycholic acid 3-sulfate and glycodeoxycholic acid 3-O-β-glucuronide are proposed as potential biomarkers based on observed four- to fivefold increase in plasma AUC (vs placebo), following administration of a compound known to present as an OATP1B1/3 inhibitor in vitro.

A fully automated and validated human plasma LC–MS/MS assay for endogenous OATP biomarkers coproporphyrin-I and coproporphyrin-III

Aim: A validated LC–MS/MS assay for the quantitation of coproporphyrin-I and -III (CP-I, CP-III) in human plasma has been developed to understand the utility of both as possible endogenous biomarkers for organic anion-transporting polypeptides (OATP)-mediated drug–drug interactions (DDIs). Materials and Methods: Human plasma extracts were analyzed for CP-I and CP-III using a Sciex API 6500+ mass spectrometer. Results: The assay was utilized for plasma samples from a clinical DDI study involving a new chemical entity that presented as an OATP inhibitor in vitro. A formal DDI study, with a probe drug (atorvastatin), was also included as part of the clinical study. Conclusion: Changes in CP-I area under the plasma concentration versus time curve (AUC0–48 h) were observed, which were similar to the AUC ratio obtained with atorvastatin. These results support the idea that plasma CP-I may have utility in Phase I by supporting the rapid assessment of OATP inhibition risk.

A highly selective and sensitive LC–MS/HRMS assay for quantifying coproporphyrins as organic anion-transporting peptide biomarkers

Aim: Coproporphyrin-I (CP-I) and coproporphyrin-III (CP-III) in plasma and urine have been proposed as biomarkers for assessing drug–drug interactions involving hepatic drug transporters such as organic anion-transporting peptides (OATP), 1B1 and 1B3. Materials & methods: Plasma and urine extracts were analyzed for CP-I/CP-III using a TripleTOF API6600 mass spectrometer. Results: Previously unreported, CP-I/CP-III doubly charged ions (m/z 328.14) were used as precursor ions to improve the assay sensitivity and selectivity over the singly charged precursor ions (m/z 655.28). Levels of CP-I and CP-III measured ranged 0.45–1.1 and 0.050–0.50 ng/ml in plasma and 5–35 and 1–35 ng/ml in urine, respectively. Conclusion: The described highly selective and sensitive CP-I/CP-III LC–HRMS assay offers options for earlier characterization and clinical safety projections for OATP1B1/3-mediated drug–drug interactions along with pharmacokinetic analyses of a new chemical entity as part of first-in-human clinical studies.