Impact of genetic polymorphism on drug-drug interactions mediated by cytochromes: a general approach

Pharmacogenetic guided drug therapy - how to deal with phenoconversion in polypharmacy

INTRODUCTION

The prevalence of polypharmacy and the increasing availability of pharmacogenetic information in clinical practice have raised the prospect of data-driven clinical decision-making when addressing the issues of drug-drug interactions and genetic polymorphisms in metabolizing enzymes. Inhibition of metabolizing enzymes in drug interactions can lead to genotype-phenotype discrepancies (phenoconversion) that reduce the relevance of individual pharmacogenetic information.

AREAS COVERED

The aim of this review is to provide an overview of existing models of phenoconversion, and we discuss how phenoconversion models may be developed to estimate joint drug-interactions and genetic effects. Based on a literature search in PubMed, Google Scholar, and reference lists from review articles, we provide an overview of the current models of phenoconversion. The currently applied phenoconversion models are presented and discussed to predict the effects of drug-drug interactions while accounting for the pharmacogenetic status of patients.

EXPERT OPINION

While pharmacogenetic-dose recommendations alone are most relevant for rare and extreme genotypes, phenoconversion may increase the prevalence of these phenotypes. Therefore, in polypharmacy conditions, phenoconversion assessment is especially important for personalized drug therapy.

Phenotypic Models of Drug-Drug-Gene Interactions Mediated by Cytochrome Drug-Metabolizing Enzymes

Genetic polymorphisms in drug metabolizing enzymes and drug-drug interactions are major sources of inadequate drug exposure and ensuing adverse effects or insufficient responses. The current challenge in assessing drug-drug gene interactions (DDGIs) for the development of precise dose adjustment recommendation systems is to take into account both simultaneously. Here, we analyze the static models of DDGI from in vivo data and focus on the concept of phenoconversion to model inhibition and genetic polymorphisms jointly. These models are applicable to datasets where pharmacokinetic information is missing and are being used in clinical support systems and consensus dose adjustment guidelines. We show that all such models can be handled by the same formal framework, and that models that differ at first sight are all versions of the same linear phenoconversion model. This model includes the linear pharmacogenetic and inhibition models as special cases. We highlight present challenges in this endeavor and the open issues for future research in developing DDGI models for recommendation systems.

Therapeutic Drug Monitoring and Pharmacogenetic Testing as Guides to Psychotropic Drug Dose Adjustment: An Observational Study

To avoid the failures in therapy with psychotropic drugs, treatments can be personalized by applying the results of therapeutic drug monitoring and pharmacogenetic testing. The objective of the present single-center observational study was to describe the changes in psychotropic drug management prompted by therapeutic drug monitoring and pharmacogenetic testing, and to compare the effective drug concentration based on metabolic status with the dose predicted using an in silico decision tool for drug-drug interactions. The study was conducted in psychiatry wards at Lille University Hospital (Lille, France) between 2016 and 2020. Patients with data for at least one therapeutic drug monitoring session or pharmacogenetic test were included. Blood tests were performed for 490 inpatients (mainly indicated by treatment monitoring or failure) and mainly concerned clozapine (21.4%) and quetiapine (13.7%). Of the 617 initial therapeutic drug monitoring tests, 245 (40%) complied with good sampling practice. Of the patients, 51% had a drug concentration within the therapeutic range. Regardless of the drug concentration, the drug management did not change in 83% of cases. Thirty patients underwent pharmacogenetic testing (twenty-seven had also undergone therapeutic drug monitoring) for treatment failure; the plasma drug concentration was outside the reference range in 93% of cases. The patient's metabolic status explained the treatment failure in 12 cases (40%), and prompted a switch to a drug metabolized by another CYP450 pathway in 5 cases (42%). Of the six tests that could be analyzed with the in silico decision tool, all of the drug concentrations after adjustment were included in the range estimated by the tool. Knowledge of a patient's drug concentration and metabolic status (for CYD2D6 and CYP2C19) can help clinicians to optimize psychotropic drug adjustment. Drug management can be optimized with good sampling practice, support from a multidisciplinary team (a physician, a geneticist, and clinical pharmacist), and decision support tools.

Assessment of community pharmacists' knowledge about drug-drug interactions in Jeddah, Saudi Arabia

Background: Drug-drug interactions (DDIs) have the potential to result in severe adverse drug events and profoundly affect patient outcomes. The pivotal role community pharmacists assume in recognizing and effectively managing these interactions necessitates a comprehensive understanding and heightened awareness of their implications. Such knowledge and awareness among community pharmacists are fundamental for ensuring the delivery of safe and efficacious care to patients. Aim: This study aimed to assess the knowledge of community pharmacists in Jeddah, Saudi Arabia, regarding drug-drug interactions (DDIs). Method: A cross-sectional survey was administered to a cohort of 147 community pharmacists through the utilization of a self-administered questionnaire. The questionnaire encompassed a comprehensive range of 30 multiple-choice questions, encompassing various facets pertaining to drug-drug interactions (DDIs). Results: A total of 147 community pharmacists working in Jeddah City, Saudi Arabia, completed the survey. The majority of them were male (89.1%, n = 131), and had bachelor's degrees in pharmacy. Results showed that the lowest correct response of DDIs was between Theophylline/Omeprazole, while the highest was between amoxicillin and acetaminophen. Results revealed that among the 28 drug pairs, only six pairs were determined correctly by most participants. The study found that majority of the studied community pharmacist could not determine the correct answer on drug-drug interaction knowledge, as also seen with the measured below half mean DDIs knowledge of 38.22 ± 22.0 (min = 0, max = 89.29, median = 35.71). Conclusion: The study highlights the need for ongoing training and education programs for community pharmacists in Saudi Arabia to enhance their knowledge and understanding of DDIs, ultimately leading to improved patient care and safety.

Integrated Use of In Vitro and In Vivo Information for Comprehensive Prediction of Drug Interactions Due to Inhibition of Multiple CYP Isoenzymes

BACKGROUND

Mechanistic static pharmacokinetic (MSPK) models are simple, have fewer data requirements, and have broader applicability; however, they cannot use in vitro information and cannot distinguish the contributions of multiple cytochrome P450 (CYP) isoenzymes and the hepatic and intestinal first-pass effects appropriately. We aimed to establish a new MSPK analysis framework for the comprehensive prediction of drug interactions (DIs) to overcome these disadvantages.

METHODS

Drug interactions that occurred by inhibiting CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A in the liver and CYP3A in the intestine were simultaneously analyzed for 59 substrates and 35 inhibitors. As in vivo information, the observed changes in the area under the concentration-time curve (AUC) and elimination half-life (t1/2), hepatic availability, and urinary excretion ratio were used. As in vitro information, the fraction metabolized (fm) and the inhibition constant (Ki) were used. The contribution ratio (CR) and inhibition ratio (IR) for multiple clearance pathways and hypothetical volume (VHyp) were inferred using the Markov Chain Monte Carlo (MCMC) method.

RESULT

Using in vivo information from 239 combinations and in vitro 172 fm and 344 Ki values, changes in AUC, and t1/2 were estimated for all 2065 combinations, wherein the AUC was estimated to be more than doubled for 602 combinations. Intake-dependent selective intestinal CYP3A inhibition by grapefruit juice has been suggested. By separating the intestinal contributions, DIs after intravenous dosing were also appropriately inferred.

CONCLUSION

This framework would be a powerful tool for the reasonable management of various DIs based on all available in vitro and in vivo information.

Drug-Drug-Gene Interactions in Cardiovascular Medicine

Cardiovascular disease remains a leading cause of both morbidity and mortality worldwide. It is widely accepted that both concomitant medications (drug-drug interactions, DDIs) and genomic factors (drug-gene interactions, DGIs) can influence cardiovascular drug-related efficacy and safety outcomes. Although thousands of DDI and DGI (aka pharmacogenomic) studies have been published to date, the literature on drug-drug-gene interactions (DDGIs, cumulative effects of DDIs and DGIs) remains scarce. Moreover, multimorbidity is common in cardiovascular disease patients and is often associated with polypharmacy, which increases the likelihood of clinically relevant drug-related interactions. These, in turn, can lead to reduced drug efficacy, medication-related harm (adverse drug reactions, longer hospitalizations, mortality) and increased healthcare costs. To examine the extent to which DDGIs and other interactions influence efficacy and safety outcomes in the field of cardiovascular medicine, we review current evidence in the field. We describe the different categories of DDIs and DGIs before illustrating how these two interact to produce DDGIs and other complex interactions. We provide examples of studies that have reported the prevalence of clinically relevant interactions and the most implicated cardiovascular medicines before outlining the challenges associated with dealing with these interactions in clinical practice. Finally, we provide recommendations on how to manage the challenges including but not limited to expanding the scope of drug information compendia, interaction databases and clinical implementation guidelines (to include clinically relevant DDGIs and other complex interactions) and work towards their harmonization; better use of electronic decision support tools; using big data and novel computational techniques; using clinically relevant endpoints, preemptive genotyping; ensuring ethnic diversity; and upskilling of clinicians in pharmacogenomics and personalized medicine.

Quantitative Prediction of Adverse Event Probability Due to Pharmacokinetic Interactions

INTRODUCTION

Iatrogeny due to drug-drug interactions is insufficiently documented, due to the high number of possible combinations.

OBJECTIVE

This study aimed to design a simple but general method to predict the variation of adverse events (AE) frequency due to a pharmacokinetic or pharmacodynamic interaction.

METHODS

Three prediction models were designed using a logistic probability density function. Each prediction model was based on three components: the AE odds ratio of each drug in the combination, and the area under the curve ratio (Rauc) of the pharmacokinetic interaction, if any. Pharmacodynamic interaction was assumed to be additive on logit scale. Rauc was predicted using a well-validated mechanistic static model, freely available online. No combination study is required. The method was evaluated against a wide range of AEs (28 High Level Terms) and 211 drug combinations (involving 43 victim drugs and 55 perpetrators), by comparing the observed and predicted frequencies. The observed odds ratios were estimated with a disproportionality analysis from the FDA Adverse Event Reporting System, using an approach that minimizes biases.

RESULTS

With the best model, the rate of prediction considered as correct (within 50-200% of the observed value) was 72%, and the bias was negligible (-5%). The AE odds ratio due to pharmacokinetic and pharmacodynamic interactions was equally well predicted.

CONCLUSIONS

A simple workflow to implement the method in practice is proposed. This method may help to foresee and to anticipate the harmful consequences associated with drug-drug interactions, at virtually no experimental cost, when the odds ratio of an AE is known for each drug alone and the AUC ratio is known or predicted by a suitable model.

Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp^® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.

Interaction between Omeprazole and Gliclazide in Relation to CYP2C19 Phenotype

The antidiabetic drug gliclazide is partly metabolized by CYP2C19, the main enzyme involved in omeprazole metabolism. The aim of the study was to explore the interaction between omeprazole and gliclazide in relation to CYP2C19 phenotype using physiologically based pharmacokinetic (PBPK) modeling approach. Developed PBPK models were verified using in vivo pharmacokinetic profiles obtained from a clinical trial on omeprazole-gliclazide interaction in healthy volunteers, CYP2C19 normal/rapid/ultrarapid metabolizers (NM/RM/UM). In addition, the association of omeprazole cotreatment with gliclazide-induced hypoglycemia was explored in 267 patients with type 2 diabetes (T2D) from the GoDARTS cohort, Scotland. The PBPK simulations predicted 1.4–1.6-fold higher gliclazide area under the curve (AUC) after 5-day treatment with 20 mg omeprazole in all CYP2C19 phenotype groups except in poor metabolizers. The predicted gliclazide AUC increased 2.1 and 2.5-fold in intermediate metabolizers, and 2.6- and 3.8-fold in NM/RM/UM group, after simulated 20-day dosing with 40 mg omeprazole once and twice daily, respectively. The predicted results were corroborated by findings in patients with T2D which demonstrated 3.3-fold higher odds of severe gliclazide-induced hypoglycemia in NM/RM/UM patients concomitantly treated with omeprazole. Our results indicate that omeprazole may increase exposure to gliclazide and thus increase the risk of gliclazide-associated hypoglycemia in the majority of patients.

Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools’ respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools’ respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (R_AUC) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM (The National Agency for the Safety of Medicines and Health Products), GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P’s R_AUC: 90.0%, 44.2% and 75.0% for R_AUC ≤ 0.5; R_AUC 0.5–2 and R_AUC > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the R_AUC category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments.

Interaction of Phytocompounds of Echinacea purpurea with ABCB1 and ABCG2 Efflux Transporters

Preparations of Echinacea purpurea (E. purpurea) are widely used for the management of upper respiratory infections, influenza, and common cold, often in combination with other conventional drugs. However, the potential of phytochemical constituents of E. purpurea to cause herb-drug interactions via ABCB1 and ABCG2 efflux transporters remains elusive. The purpose of this study was to investigate the impact of E. purpurea-derived caffeic acid derivatives (cichoric acid and echinacoside) and tetraenes on the mRNA and protein expression levels as well as on transport activity of ABCB1 and ABCG2 in intestinal (Caco-2) and liver (HepG2) cell line models. The safety of these compounds was investigated by estimating EC₂₀ values of cell viability assays in both cell lines. Regulation of ABCB1 and ABCG2 protein in these cell lines were analyzed after 24 h exposure to the compounds at 1, 10, and 50 μg/mL. Bidirectional transport of 0.5 μg/mL Hoechst 33342 and 5 μM rhodamine across Caco-2 monolayer and profiling for intracellular concentrations of the fluorophores in both cell lines were conducted to ascertain inhibition effects of the compounds. Cichoric acid showed no cytotoxic effect, while the EC₂₀ values of tetraenes and echinacoside were 45.0 ± 3.0 and 52.0 ± 4.0 μg/mL in Caco-2 cells and 28.0 ± 4.3 and 62.0 ± 9.9 μg/mL in HepG2 cells, respectively. In general, the compounds showed heterogeneous induction of ABCB1 with the strongest 3.6 ± 1.2-fold increase observed for 10 μg/mL tetraenes in Caco-2 cells (p < 0.001). However, the compounds did not induce ABCG2. None of the phytocompounds inhibited significantly net flux of the fluorophores across Caco-2 monolayers. Overall, tetraenes moderately induced ABCB1 but not ABCG2 in Caco-2 and HepG2 cells while no compound significantly inhibited activity of these transporters at clinically relevant concentration to cause herb-drug interactions.

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data

'Artificial Intelligence' (AI) has recently had a profound impact on areas such as image and speech recognition, and this progress has already translated into practical applications. However, in the drug discovery field, such advances remains scarce, and one of the reasons is intrinsic to the data used. In this review, we discuss aspects of, and differences in, data from different domains, namely the image, speech, chemical, and biological domains, the amounts of data available, and how relevant they are to drug discovery. Improvements in the future are needed with respect to our understanding of biological systems, and the subsequent generation of practically relevant data in sufficient quantities, to truly advance the field of AI in drug discovery, to enable the discovery of novel chemistry, with novel modes of action, which shows desirable efficacy and safety in the clinic.

Pharmacokinetic Disposition Difference Between Cyclosporine and Voclosporin Drives Their Distinct Efficacy and Safety Profiles in Clinical Studies

Background

Voclosporin, a more potent derivative of cyclosporine, has been studied extensively in patients with immunologic disorders such as psoriasis, organ transplantation, uvetitis and lupus nephritis. Although better tolerated and safer than cyclosporine, voclosporin is inferior to cyclosporine in treating psoriasis, non-inferior to tacrolimus in organ transplantation and efficacious in treating lupus nephritis.

Methods

The pharmacokinetic dispositions of voclosporin and cyclosporine in central and peripheral compartments were analyzed and correlated with their distinct clinical efficacy and safety profiles.

Results

Both drugs demonstrated non-linear pharmacokinetics with increasing doses, more prominently at lower doses of voclosporin than at 10-fold higher doses of cyclosporine. Repeated lower dosing of voclosporin produced preferential calcineurin inhibition in and near blood circulation, leading to relatively lower cardiovascular and renal adverse effects but inferior efficacy for psoriasis compared to cyclosporine. With 10-fold higher plasma levels and deeper tissue penetration, cyclosporine has more prevalent renal and cardiac toxicities but superior efficacy to treat psoriasis.

Conclusion

Although the two drugs are similar in structure and mechanism of action, the high potency and low dose compounded by the non-linear disposition of voclosporin resulted in more systemic versus local calcineurin inhibition than with cyclosporine. The dispositional difference between voclosporin and cyclosporine accounted for the puzzling efficacy and safety observations in different patients and was the basis for their optimal and differential use in treating diverse immunologic disorders.

Drug-drug-gene interactions and adverse drug reactions

The economic and health burden caused by adverse drug reactions has increased dramatically in the last few years. This is likely to be mediated by increasing polypharmacy, which increases the likelihood for drug–drug interactions. Tools utilized by healthcare practitioners to flag potential adverse drug reactions secondary to drug–drug interactions ignore individual genetic variation, which has the potential to markedly alter the severity of these interactions. To date there have been limited published studies on impact of genetic variation on drug–drug interactions. In this review, we establish a detailed classification for pharmacokinetic drug–drug–gene interactions, and give examples from the literature that support this approach. The increasing availability of real-world drug outcome data linked to genetic bioresources is likely to enable the discovery of previously unrecognized, clinically important drug–drug–gene interactions.

Identification of Cytochrome P450-Mediated Drug-Drug Interactions at Risk in Cases of Gene Polymorphisms by Using a Quantitative Prediction Model

BACKGROUND AND OBJECTIVE

The magnitude of drug-drug interactions mediated by cytochrome P450 (CYP) may depend on the genotype of polymorphic cytochromes. The objective of this study was to identify drug-drug interactions with greater magnitude in CYP variant groups than in extensive metabolizers.

METHODS

The in-vivo mechanistic static model was used to predict the area under the curve ratio of drug-drug interactions. Five cytochromes (CYP3A4/5, 2D6, 2C9, 2C19, 1A2) and five groups of genotypes for each polymorphic cytochrome (CYP2D6, 2C9, 2C19) were considered. The area under the curve ratios were calculated for all combinations and all genotypes for 196 substrates and 96 inhibitors. Among the strongest interactions (area under the curve ratio greater than 5), two levels of gene sensitivity of drug-drug interactions were defined: the intermediate sensitivity, with a three- to five-fold stronger interaction in genotype groups other than in extensive metabolizers, and the high sensitivity, with a more than five-fold stronger interaction than in genotype groups other than extensive metabolizers.

RESULTS

A red list of 104 interactions with a sensitivity greater than 3, involving 13 substrates and 24 interactors was obtained. There were 59 and 45 cases of high and intermediate sensitivity, respectively. The genotypes associated with a high sensitivity were CYP2D6 *3-8 *3-8 (sensitivity up to 24.3) and CYP2C19 *2-3*2-3 (sensitivity up to 37.8).

CONCLUSIONS

A cytochrome polymorphism may lead to major drug-drug interactions in poor metabolizers, while these interactions may not be significant in extensive metabolizers. Among the 104 cases studied, the interaction could be of ca. 30-fold larger magnitude in the worst case. Genotyping of the patient and/or therapeutic drug monitoring of the substrate should be carried out when an association mentioned in the red list is prescribed. The concept of gene sensitivity of drug-drug interactions appears promising for the development of precision medicine.

Physical Performance and Quality of Life in Older Adults: Is There Any Association between Them and Potential Drug Interactions in Polymedicated Octogenarians

Older adults are at increased risk of several cytochrome P450 (CYP) drug interactions that can result in drug toxicity, reduced pharmacological effect, and adverse drug reactions. This study aimed to assess the prevalence of potential CYP interactions referring to the most clinically relevant drugs and exploring the relationship between them and quality of life and physical performance in Spanish octogenarians. Institutionalized and community-dwelling octogenarians (n = 102) treated at three primary care centers, were recruited by a research nurse. Anthropometric measurements, chronic diseases, prescribed drugs, quality of life, physical performance, mobility skills, hand grip strength and cognitive status data were collected. Potential CYP drug-drug interactions (DDIs) were selected referring to the main CYP implicated in their metabolism. The 72.2% of recruited octogenarians presented potentially inappropriate CYP inhibitor-substrate or CYP inductor-substrate combinations. Analyzing the EuroQol Visual Analogue scale (EQ-VAS) results, patients with a potential CYP DDI perceived worse health status than patients without it (p = 0.004). In addition, patients with a potential CYP DDI presented worse exercise capacity, kinesthetic abilities, or mobility than those who didn’t present a potential interaction (p = 0.01, p = 0.047, and p = 0.02, respectively). To investigate and control factors associated with loss of muscle strength and poor quality of life, polypharmacy and DDIs could help institutions in the management of physical frailty.

Integrating anatomo-physiological changes and pharmacogenomics in anti-infective therapy management: is it a major concern?

Success of anti-infective therapy is a major challenge in some patients given anatomo-physiological changes and genetic variations. In this case anecdote, we report the management strategy of a patient suffering from chronic pulmonary aspergillosis in a context of anorexia nervosa and genetic polymorphism.

Successful and safe long-term treatment of cerebral aspergillosis with high-dose voriconazole guided by therapeutic drug monitoring

We report the case of a patient who had cerebral aspergillosis after otorhinolaryngologic surgery and who was successfully and safely treated with high-dose voriconazole (200 mg q6h) for more than 1 year thanks to a TDM-guided approach coupled with pharmacological review and with genotyping of CYP2C19 polymorphisms. The findings support the idea that personalized medicine based on TDM coupled with the need of avoiding drug-drug interactions may be helpful for maximizing the net benefit (probability of efficacy vs. probability of adverse events) of voriconazole in the management of long-term treatment of cerebral aspergillosis.

Complex Drug-Drug-Gene-Disease Interactions Involving Cytochromes P450: Systematic Review of Published Case Reports and Clinical Perspectives

Drug pharmacokinetics (PK) is influenced by multiple intrinsic and extrinsic factors, among which concomitant medications are responsible for drug-drug interactions (DDIs) that may have a clinical relevance, resulting in adverse drug reactions or reduced efficacy. The addition of intrinsic factors affecting cytochromes P450 (CYPs) activity and/or expression, such as genetic polymorphisms and diseases, may potentiate the impact and clinical relevance of DDIs. In addition, greater variability in drug levels and exposures has been observed when such intrinsic factors are present in addition to concomitant medications perpetrating DDIs. This variability results in poor predictability of DDIs and potentially dramatic clinical consequences. The present review illustrates the issue of complex DDIs using systematically searched published case reports of DDIs involving genetic polymorphisms, renal impairment, cirrhosis, and/or inflammation. Current knowledge on the impact of each of these factors on drug exposure and DDIs is summarized and future perspectives for the management of such complex DDIs in clinical practice are discussed, including the use of advanced Computerized Physician Order Entry (CPOE) systems, the development of model-based dose optimization strategies, and the education of healthcare professionals with respect to personalized medicine.

Impact of CYP2D6 Functional Allelic Variations on Phenoconversion and Drug-Drug Interactions

We investigated whether CYP2D6 extensive metabolizers carrying a nonfunctional allele are at higher risk of phenoconversion to poor metabolizers in the presence of CYP2D6 inhibitors. Seventeen homozygous carriers of two fully-functional alleles and 17 heterozygous carriers of one fully-functional and one nonfunctional allele participated in this trial. Dextromethorphan 5 mg and tramadol 10 mg were given at each of the three study sessions. CYP2D6 was inhibited by duloxetine 60 mg (session 2) and paroxetine 20 mg (session 3). A higher rate of phenoconversion to intermediate metabolizers with duloxetine (71% vs. 25%, P = 0.009) and to poor metabolizers with paroxetine (94% vs. 56%, P = 0.011) was observed in heterozygous than homozygous extensive metabolizers. The magnitude of drug-drug interaction between dextromethorphan and paroxetine was higher in homozygous than in heterozygous subjects (14.6 vs. 8.5, P < 0.028). Our study suggests that genetic extensive metabolizers may not represent a homogenous population and that available genetic data should be considered when addressing drug-drug interactions in clinical practice.

Concordance between actual and pharmacogenetic predicted desvenlafaxine dose needed to achieve remission in major depressive disorder: a 10-week open-label study

Supplemental Digital Content is available in the text.

Background

Pharmacogenetic-based dosing support tools have been developed to personalize antidepressant-prescribing practice. However, the clinical validity of these tools has not been adequately tested, particularly for specific antidepressants.

Objective

To examine the concordance between the actual dose and a polygene pharmacogenetic predicted dose of desvenlafaxine needed to achieve symptom remission.

Materials and methods

A 10-week, open-label, prospective trial of desvenlafaxine among Caucasian adults with major depressive disorder (n=119) was conducted. Dose was clinically adjusted and at the completion of the trial, the clinical dose needed to achieve remission was compared with the predicted dose needed to achieve remission.

Results

Among remitters (n=95), there was a strong concordance (Kendall’s τ-b=0.84, P=0.0001; Cohen’s κ=0.82, P=0.0001) between the actual and the predicted dose need to achieve symptom remission, showing high sensitivity (≥85%), specificity (≥86%), and accuracy (≥89%) of the tool.

Conclusion

Findings provide initial evidence for the clinical validity of a polygene pharmacogenetic-based tool for desvenlafaxine dosing.

Evaluation of Cytochrome P450 3A4-Mediated Drug-Drug Interaction Potential for Cobimetinib Using Physiologically Based Pharmacokinetic Modeling and Simulation

BACKGROUND AND OBJECTIVES

Cobimetinib is eliminated mainly through cytochrome P450 (CYP) 3A4-mediated hepatic metabolism in humans. A clinical drug-drug interaction (DDI) study with the potent CYP3A4 inhibitor itraconazole resulted in an approximately sevenfold increase in cobimetinib exposure. The DDI risk for cobimetinib with other CYP3A4 inhibitors and inducers needs to be assessed in order to provide dosing instructions.

METHODS

A physiologically based pharmacokinetic (PBPK) model was developed for cobimetinib using in vitro data. It was then optimized and verified using clinical pharmacokinetic data and itraconazole-cobimetinib DDI data. The contribution of CYP3A4 to the clearance of cobimetinib in humans was confirmed using sensitivity analysis in a retrospective simulation of itraconazole-cobimetinib DDI data. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors and inducers on cobimetinib pharmacokinetics.

RESULTS

The PBPK model described cobimetinib pharmacokinetic profiles after both intravenous and oral administration of cobimetinib well and accurately simulated the itraconazole-cobimetinib DDI. Sensitivity analysis suggested that CYP3A4 contributes ~78 % of the total clearance of cobimetinib. The PBPK model predicted no change in cobimetinib exposure (area under the plasma concentration-time curve, AUC) with the weak CYP3A inhibitor fluvoxamine and a three to fourfold increase with the moderate CYP3A inhibitors, erythromycin and diltiazem. Similarly, cobimetinib exposure in the presence of strong (rifampicin) and moderate (efavirenz) CYP3A inducers was predicted to decrease by 83 and 72 %, respectively.

CONCLUSION

This study demonstrates the value of using PBPK simulation to assess the clinical DDI risk inorder to provide dosing instructions with other CYP3A4 perpetrators.

Pharmacogenetics of drug-drug interaction and drug-drug-gene interaction: a systematic review on CYP2C9, CYP2C19 and CYP2D6

Currently, most guidelines on drug-drug interaction (DDI) neither consider the potential effect of genetic polymorphism in the strength of the interaction nor do they account for the complex interaction caused by the combination of DDI and drug-gene interaction (DGI) where there are multiple biotransformation pathways, which is referred to as drug-drug-gene interaction (DDGI). In this systematic review, we report the impact of pharmacogenetics on DDI and DDGI in which three major drug-metabolizing enzymes - CYP2C9, CYP2C19 and CYP2D6 - are central. We observed that several DDI and DDGI are highly gene-dependent, leading to a different magnitude of interaction. Precision drug therapy should take pharmacogenetics into account when drug interactions in clinical practice are expected.

Comparison of the static in vivo approach to a physiologically based pharmacokinetic approach for metabolic drug–drug interactions prediction

Background: The in vivo mechanistic static model (IMSM) and the physiologically based pharmacokinetic (PBPK) model are two approaches used to predict the magnitude of drug–drug interactions (DDIs). The aim of this study was to evaluate the performance of IMSM and to compare IMSM with the PBPK approach implemented in Simcyp. Methods: The predictive performances of IMSM were evaluated on a panel of 628 DDIs. Subsequently, the IMSM and PBPK approaches were compared on a set of 104 DDIs. Results: The IMSM yielded 85% of predictions within 1.5-fold of the observed value on the 628 DDIs panel. The predictive performances of IMSM were better than those of the PBPK approach (median fold error 1 vs 0.86 on 104 studies; p = 0.02). Conclusion: The IMSM approach is an alternative tool for metabolic DDIs prediction.

A Prediction Model of Drug Exposure in Cirrhotic Patients According to Child-Pugh Classification

BACKGROUND AND OBJECTIVE

Prediction of drug clearance in liver cirrhosis patients is currently based on in vitro-in vivo extrapolation and physiologically-based pharmacokinetic models. No static model for this purpose has been described. The objectives of this study were to (1) derive a static model for predicting drug exposure in cirrhotic patients, and (2) to evaluate the model on a large set of published data.

METHODS

The impact of cirrhosis was characterized by the ratio of the total and unbound drug area under the concentration-time curve (AUC) in cirrhotic patients to the AUC measured in healthy subjects These ratios were predicted for Child-Pugh classes A, B, and C. The AUC ratios observed in published data were compared with AUC ratios predicted by the model.

RESULTS

Among 171 drugs examined, 83 published AUC ratios for 45 drugs in cirrhotic patients were available for analysis. The mean ± standard deviation relative prediction error for the total and unbound AUC ratios was 0.22 ± 0.58 and 0.24 ± 0.56, respectively. There were four outliers among the 83 predicted values. Simulations showed that the prediction error was negligible provided that the hepatic extraction coefficient was less than 0.8.

CONCLUSIONS

For mild and moderate cirrhosis (classes A and B), the predicted unbound AUC ratio is typically approximately 2 and 3.5, respectively, for most drugs. In the absence of data in cirrhotic patients, the drug dose might be empirically reduced by these factors. In severe cirrhosis (class C), our model may help clinicians to adjust their prescriptions.

Decision support in addiction: The development of an e-health tool to assess and prevent risk of fatal overdose. The ORION Project

BACKGROUND AND OBJECTIVE

The application of e-health technology to the field of substance use disorders is at a relatively early stage, and methodological quality is still variable. Few have explored the extent of utilization of communication technology in exploring risk perception by patients enrolled in substance abuse services. The Overdose RIsk InfOrmatioN (ORION) project is a European Commission funded programme, aimed to develop and pilot an e-health psycho-educational tool to provide information to drug using individuals about the risks of suffering a drug overdose.

METHODS

In this article, we report on phase 1 (risk estimation), phase 2 (design), and phase 3 (feasibility) of the ORION project.

RESULTS

The development of ORION e-health tool underlined the importance of an evidence-based intervention aimed in obtaining reliable evaluation of risk. The ORION tool supported a decision making process aimed at influencing the substance users' self-efficacy and the degree to which the substance users' understand risk factors. Therefore, its innovative power consisted in translating risks combination into a clear estimation for the user who will then appear more likely to be interested in his/her risk perception.

CONCLUSION

Exploratory field testing and validation confirmed the next stage of evaluation, namely, collection of routine patient samples in study clinics. The associations between risk perception of overdose, engagement with the ORION tool and willingness to alter overdose risk factors, in a clinical setting across various EU member states will further confirm the ORION tool's generalisability and effectiveness.

Assessment of drug metabolism enzyme and transporter pharmacogenetics in drug discovery and early development: perspectives of the I-PWG

Genetic variants of drug metabolism enzymes and transporters can result in high pharmacokinetic and pharmacodynamic variability, unwanted characteristics of efficacious and safe drugs. Ideally, the contributions of these enzymes and transporters to drug disposition can be predicted from in vitro experiments and in silico modeling in discovery or early development, and then be utilized during clinical development. Recently, regulatory agencies have provided guidance on the preclinical investigation of pharmacogenetics, for application to clinical drug development. This white paper summarizes the results of an industry survey conducted by the Industry Pharmacogenomics Working Group on current practice and challenges with using in vitro systems and in silico models to understand pharmacogenetic causes of variability in drug disposition.

Improved Antidepressant Remission in Major Depression via a Pharmacokinetic Pathway Polygene Pharmacogenetic Report

Objective

Major depressive disorder (MDD) is projected to be a leading cause of disability globally by 2030. Only a minority of patients remit with antidepressants. If assay of polymorphisms influencing central nervous system (CNS) bioavailability could guide prescribers to more effectively dose patients, remission rates may improve and the burden of disease from MDD reduce. Hepatic and blood brain barrier (BBB) polymorphisms appear to influence antidepressant CNS bioavailability.

Methods

A 12-week prospective double blind randomized genetically guided versus unguided trial of antidepressant dosing in Caucasian adults with MDD (n=148) was conducted.

Results

Subjects receiving genetically guided prescribing had a 2.52-fold greater chance of remission (95% confidence interval [CI]=1.71–3.73, z=4.66, p<0.0001). The number needed to genotype (NNG)=3 (95% CI=1.7–3.5) to produce an additional remission.

Conclusion

These data suggest that a pharmacogenetic dosing report (CNSDose^®) improves antidepressant efficacy. The effect size was sufficient that translation to clinical care may arise if results are independently replicated.

How common are drug and gene interactions? Prevalence in a sample of 1143 patients with CYP2C9, CYP2C19 and CYP2D6 genotyping

AIM

Drug-drug interactions (DDIs) are a widely recognized major cause of adverse drug reactions, but two other newly described important types of interactions also exist: drug-gene interactions (DGIs) and drug-drug-gene interactions (DDGIs). A drug-gene interaction occurs when a patient's genetic CYP450 type (e.g., CYP2D6 poor metabolizer) affects that patient's ability to clear a drug. A drug-drug-gene interaction occurs when the patient's CYP450 genotype and another drug in the patient's regimen (e.g., a CYP2D6 inhibitor) affect that individual's ability to clear a drug. Their prevalence has not been previously described. This pilot study investigates the frequency of DDIs, DGIs and DDGIs in a sample of CYP450 tested individuals.

MATERIALS & METHODS

The investigators conducted a retrospective analysis of 1143 individuals with known CYP2D6, CYP2C19 and CYP2C9 genotypes. Using the individuals' medication lists and YouScript(®), a software tool to analyze cumulative DDIs and DGIs, the prevalence of DDI, DGI and DDGIs was analyzed.

RESULTS

A total of 1053 potential major or substantial interactions were identified in 501 individuals. DDIs accounted for 66.1% of the total interactions. The remaining 33.9% of interactions were DGIs (14.7%) and DDGIs (19.2%). When compared with DDIs alone, DGIs and DDGIs increased the total number of potentially clinically significant interactions by 51.3%.

CONCLUSION

In the future, identifying DGIs and DDGIs may lead to a more comprehensive method of identifying individuals who are at risk for adverse drug reactions.

A low-cost, high-quality new drug discovery process using patient-derived induced pluripotent stem cells

Knockout, knock-in and conditional mutant gene-targeted mice are routinely used for disease modeling in the drug discovery process, but the human response is often difficult to predict from these models. It is believed that patient-derived induced pluripotent stem cells (iPSCs) could replace millions of animals currently sacrificed in preclinical testing and provide a route to new safer pharmaceutical products. In this review, we discuss the use of IPSCs in the drug discovery process. We highlight how they can be used to assess the toxicity and clinical efficacy of drug candidates before the latter are moved into costly and lengthy preclinical and clinical trials.

Reliability and extension of quantitative prediction of CYP3A4-mediated drug interactions based on clinical data

An approach was proposed in 2007 for quantitative predictions of cytochrome P450 (CYP)3A4-mediated drug-drug interactions. It is based on two characteristic parameters: the contribution ratio (CR; i.e., the fraction of victim drug clearance by CYP) and the inhibition ratio (IR) of the inhibitor. Knowledge of these parameters allows forecasting of the ratio between the area under the plasma concentration-time curve (AUC) of the victim drug when given with the inhibitor and the AUC of the victim drug when it is given alone. So far, these parameters were established for 21 substrates and 17 inhibitors. The goals of our study were to test the assumption of substrate independence of the potency of inhibitors in vivo and to estimate the CR and IR for an extended list of substrates and inhibitors of CYP3A4. The assumption of independence of IRs from the substrate was evaluated on a set of eight victim drugs and eight inhibitors. Forty-four AUC ratios were available. This assumption was rejected in four cases, but it did not result in more than a twofold error in AUC ratio predictions. The extended list of substrates and inhibitors was defined by a thorough literature search. Fifty-nine AUC ratios were available for the global analysis. Final estimates of CRs and IRs were obtained for 37 substrates and 25 inhibitors, respectively. The mean prediction error of the ratios was 0.02, while the mean absolute prediction error was 0.58. Predictive distributions for 917 possible interactions were obtained, giving detailed information on some drugs or inhibitors that have been poorly studied so far.