Physiologically based pharmacokinetic-quantitative systems toxicology and safety (PBPK-QSTS) modeling approach applied to predict the variability of amitriptyline pharmacokinetics and cardiac safety in populations and in individuals

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

The evolving role of investigative toxicology in the pharmaceutical industry

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

Pharmacogenomics and Drug-Induced Phenoconversion Informed Medication Safety Review in the Management of Pain Control and Quality of Life: A Case Report

Utilizing pharmacogenomics (PGx) and integrating drug-induced phenoconversion to guide opioid therapies could improve the treatment response and decrease the occurrence of adverse drug events. Genetics contribute to the interindividual differences in opioid response. The purpose of this case report highlights the impact of a PGx-informed medication safety review, assisted by a clinical decision support system, in mitigating the drug–gene and drug–drug–gene interactions (DGI and DDGI, respectively) that increase the risk of an inadequate drug response and adverse drug events (ADEs). This case describes a 69-year-old female who was referred for PGx testing for uncontrolled chronic pain caused by osteoarthritis and neuropathy. The clinical pharmacist reviewed the PGx test results and medication regimen and identified several (DGIs and DDGIs, respectively) at Cytochrome P450 (CYP) 2C19 and CYP2D6. The recommendations were to: (1) switch tramadol to buprenorphine transdermal patch, an opioid with lower potential for ADEs, to mitigate a CYP2D6 DDGI; (2) gradually discontinue amitriptyline to alleviate the risk of anticholinergic side effects, ADEs, and multiple DDGIs; and (3) optimize the pregabalin. The provider and the patient agreed to implement these recommendations. Upon follow-up one month later, the patient reported an improved quality of life and pain control. Following the amitriptyline taper, the patient experienced tremors in the upper and lower extremities. When the perpetrator drug, omeprazole, was stopped, the metabolic capacity was no longer impeded; the patient experienced possible amitriptyline withdrawal symptoms due to the rapid withdrawal of amitriptyline, which was reinitiated and tapered off more slowly. This case report demonstrates a successful PGx-informed medication safety review that considered drug-induced phenoconversion and mitigated the risks of pharmacotherapy failure, ADEs, and opioid misuse.

New insights into the mechanisms underlying 5-fluorouracil-induced intestinal toxicity based on transcriptomic and metabolomic responses in human intestinal organoids

5-Fluorouracil (5-FU) is a widely used chemotherapeutical that induces acute toxicity in the small and large intestine of patients. Symptoms can be severe and lead to the interruption of cancer treatments. However, there is limited understanding of the molecular mechanisms underlying 5-FU-induced intestinal toxicity. In this study, well-established 3D organoid models of human colon and small intestine (SI) were used to characterize 5-FU transcriptomic and metabolomic responses. Clinically relevant 5-FU concentrations for in vitro testing in organoids were established using physiologically based pharmacokinetic simulation of dosing regimens recommended for cancer patients, resulting in exposures to 10, 100 and 1000 µM. After treatment, different measurements were performed: cell viability and apoptosis; image analysis of cell morphological changes; RNA sequencing; and metabolome analysis of supernatant from organoids cultures. Based on analysis of the differentially expressed genes, the most prominent molecular pathways affected by 5-FU included cell cycle, p53 signalling, mitochondrial ATP synthesis and apoptosis. Short time-series expression miner demonstrated tissue-specific mechanisms affected by 5-FU, namely biosynthesis and transport of small molecules, and mRNA translation for colon; cell signalling mediated by Rho GTPases and fork-head box transcription factors for SI. Metabolomic analysis showed that in addition to the effects on TCA cycle and oxidative stress in both organoids, tissue-specific metabolic alterations were also induced by 5-FU. Multi-omics integration identified transcription factor E2F1, a regulator of cell cycle and apoptosis, as the best key node across all samples. These results provide new insights into 5-FU toxicity mechanisms and underline the relevance of human organoid models in the safety assessment in drug development.

Improving prediction of torsadogenic risk in the CiPA in silico model by appropriately accounting for clinical exposure

Any adverse event is reliant on three properties: the appropriate pharmacology to trigger the event, the appropriate exposure of compound, and intrinsic patient factors. Each alone is necessary but insufficient to predict the event. The Comprehensive in vitro Proarrhythmia Assessment (CiPA) initiative attempts to predict the risk of torsade de pointes (TdP) by focusing on an in-silico model with thresholds determined at modest multiples of the therapeutic exposure for the parent molecule. This emphasizes the pharmacologic properties necessary for TdP but does not account for situations where clinical exposure may be higher, or where hERG potassium channel active metabolites are involved. Could accounting for clinical worst-case scenarios and metabolites, as is already standard practice in thorough QTc studies, improve the prediction algorithm? Terfenadine, a drug classed as "Intermediate" risk by CiPA, was assessed differently in the in-silico model validation. The clinical concentration of terfenadine used for the model was the exposure in the presence of metabolic inhibition representing a 14 to 40-fold increase in exposure compared to the therapeutic plasma concentration. However, several other "Intermediate" risk compounds are also known to be sensitive to metabolic inhibition and/or to have therapeutically active major metabolites, some of which are known to block hERG. Risperidone and astemizole are relevant examples. If only parent exposure is used to calculate a therapeutic window, risperidone has a relatively large multiple between clinical exposure and the hERG potency. Using this exposure of risperidone, the drug borders the "Intermediate" and "Low/No" risk categories for the CiPA in-silico model's TdP metric. The desmethyl metabolite of astemizole likely contributes significantly to the effects on cardiac repolarization, being equipotent on hERG but circulating at much higher levels than parent. Recalculating the TdP metric and margin values for terfenadine, risperidone and astemizole using the unbound concentration normally associated with treatment and a clinical worst case changes the qNet metric to higher risk values and illustrates the potential benefit to the algorithm of consistently using a clinical high exposure scenario accounting for all "hERG-active species". This exercise suggests repeating the model qualification accounting for clinical exposures and metabolites under 'stressed' scenarios would improve prediction of the TdP risk.

CardiacPBPK: A tool for the prediction and visualization of time-concentration profiles of drugs in heart tissue

BACKGROUND AND OBJECTIVE

Prediction of drug concentration in heart tissue is important in terms of drug safety and efficacy. This work presents the Open-Source CardiacPBPK platform for the prediction of the time-concentration profile of drugs, which could potentially reduce the risk of drug development failure due to cardiotoxicity. The objective of the CardiacPBPK development is to accelerate and simplify the in-silico toxicological assessment of new drugs, and to provide supportive material for the research community to use.

METHODS

The CardiacPBPK software provides a modular implementation of the PBPK model of heart tissue. It can be easily accessed via the Internet or installed locally. The graphical user interface and tabular design are easy to configure and use.

RESULTS

CardiacPBPK is a tool designed to predict and visualize the time-concentration profiles of a parent compound, and one metabolite, in venous plasma and heart tissue after oral or intravenous drug administration. CardiacPBPK is built on the R-environment framework and supports shiny application features such as interactive visualization of the results, and web applications interface by default. A shiny application refers to a computer program created with the use of shiny package in R. The application is freely available at https://github.com/jszlek/CardiacPBPK and https://sourceforge.net/projects/cardiacpbpk/. This open-source application runs on all platforms supporting R-environment (Linux, Windows, Mac OS X, Solaris).

CONCLUSIONS

We demonstrate the application of CardiacPBPK by simulating the study of amitriptyline intoxication in the case of CYP2D6 genetic polymorphism.

The optimization of methadone dosing whilst treating with rifampicin: A pharmacokinetic modeling study

BACKGROUND

The use of oral methadone in opioid substitution treatment (OST) for the management of opioid use disorder is established clinical practice. Confounding treatment is the increased risks of contracting Mycobacterium tuberculosis, the mainstay treatment of which incorporates the potent CYP 2B6 inducer rifampicin.

METHODS

This study applied pharmacokinetic modelling using virtual clinical trials, to pharmacokinetically quantify the extent and impact of rifampicin-mediated drug-drug interactions (DDI) on methadone plasma concentrations. An R-methadone model was developed and validated against 11 retrospective clinical studies prior to use in all subsequent studies. The aims were to investigate: (i) the impact of the DDI on daily methadone doses of 60 mg, 90 mg and 120 mg; (ii) dose escalation during rifampicin and (iii) dose reduction following rifampicin cessation.

RESULTS

A dose increase to 160 mg daily during rifampicin treatment phases was required to maintain peak methadone plasma concentrations within a derived therapeutic window of 80-700 ng/mL. Dose escalation prior to rifampicin initiation was not required and resulted in an increase in subjects with supra-therapeutic concentrations. However, during rifampicin cessation, a dose reduction of 10 mg every 2 days commencing prior to rifampicin cessation, ensured that most patients possessed a peak methadone plasma concentration within an optimal therapeutic window.

IMPLICATIONS

Rifampicin significantly alters methadone plasma concentrations and necessitates dose adjustments. Daily doses of almost double those used perhaps more commonly in clinical practice are required for optimal plasma concentration and careful consideration of dose reduction strategies would be required during the deinduction phase.

Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development

Model-informed drug discovery and development (MID3) is an umbrella term under which sit several computational approaches: quantitative systems pharmacology (QSP), quantitative systems toxicology (QST) and physiologically based pharmacokinetics (PBPK). QSP models are built using mechanistic knowledge of the pharmacological pathway focusing on the putative mechanism of drug efficacy; whereas QST models focus on safety and toxicity issues and the molecular pathways and networks that drive these adverse effects. These can be mediated through exaggerated on-target or off-target pharmacology, immunogenicity or the physiochemical nature of the compound. PBPK models provide a mechanistic description of individual organs and tissues to allow the prediction of the intra- and extra-cellular concentration of the parent drug and metabolites under different conditions. Information on biophase concentration enables the prediction of a drug effect in different organs and assessment of the potential for drug-drug interactions. Together, these modelling approaches can inform the exposure-response relationship and hence support hypothesis generation and testing, compound selection, hazard identification and risk assessment through to clinical proof of concept (POC) and beyond to the market.