Drug-drug interaction prediction: a Bayesian meta-analysis approach

Pharmacokinetic drug–drug interactions: an insight into recent US FDA-approved drugs for prostate cancer

Pharmacokinetic drug–drug interaction is a significant safety and efficiency concern as it results in considerable concentration changes. Drug–drug interactions are a substantial concern in anticancer drugs that possess a narrow therapeutic index. These interactions remain as the principal regulatory obstacle that can lead to termination in the preclinical stage, restrictions in the prescription, dosage adjustments or withdrawal of the drugs from the market. Drug metabolizing enzymes or transporters mediate the majority of clinically relevant drug interactions. Cancer diagnosed aged patients use multiple medications and are more prone to significant drug–drug interactions. This review provides detailed information on clinically relevant drug–drug interactions resulting from drug metabolism by enzymes and transporters with a particular emphasis on recent FDA approved antiprostate cancer drugs.

High Risk of Drug-drug interactions among Hospitalized Patients with kidney Diseases at a Nigerian Teaching Hospital: A Call for Action

Background

Potential drug-drug interactions (DDIs) are increasingly common in clinical practice, especially among individuals with chronic conditions, such as chronic kidney dysfunction. However, data relating to DDIs among chronically ill patients are limited in Nigeria. We, therefore, investigated the prevalence and pattern of DDIs among patients with kidney diseases on admission at a tertiary hospital in Lagos, Nigeria.

Materials and Methods

This was a prospective observational study involving 61 adults with kidney diseases and on admission in medical wards of the study center, over a 3-month period. Data extractions were with a purposefully designed pro forma to extract relevant data on demographic, clinical, and dosing regimens of the prescribed drugs for individual patients. Potential DDIs were identified, and their severity was rated using the MICROMEDEX^® software database (IBM^® Watson-Truven Health Analytics), which is available online with limited access.

Results

Of the 61 patients evaluated, majority were males (34; 55.7%), were elderly (26; 42.6%), and had chronic kidney disease Stage 3 (40; 65.5%). The most common cause of kidney disease was hypertension (20; 32.8%). Out of the 542 prescriptions received by the patients, potential DDI was observed in 508 (93.7%) prescriptions. Clinically significant drug interactions (CSDIs) were detected in 486 (85.7%) prescriptions. Pharmacodynamic DDIs (466; 91.7%) were the most common. Pill burden exceeding 25 pills/day was present in nine (14.8%) patients. The severities of the potential DDIs were major (135; 24.9%), moderate (333; 61.4%), and minor (38; 7.1%). Only two different potential DDIs were rated X (contraindicated).

Conclusion

Exposure to drugs with potential DDIs was very common among patients with kidney diseases. Most of the CSDIs observed were of major severity. The use of DDI checker before prescribing drugs for individuals with kidney diseases could avert clinically significant interactions.

Large-scale exploration and analysis of drug combinations

MOTIVATION

Drug combinations are a promising strategy for combating complex diseases by improving the efficacy and reducing corresponding side effects. Currently, a widely studied problem in pharmacology is to predict effective drug combinations, either through empirically screening in clinic or pure experimental trials. However, the large-scale prediction of drug combination by a systems method is rarely considered.

RESULTS

We report a systems pharmacology framework to predict drug combinations (PreDCs) on a computational model, termed probability ensemble approach (PEA), for analysis of both the efficacy and adverse effects of drug combinations. First, a Bayesian network integrating with a similarity algorithm is developed to model the combinations from drug molecular and pharmacological phenotypes, and the predictions are then assessed with both clinical efficacy and adverse effects. It is illustrated that PEA can predict the combination efficacy of drugs spanning different therapeutic classes with high specificity and sensitivity (AUC = 0.90), which was further validated by independent data or new experimental assays. PEA also evaluates the adverse effects (AUC = 0.95) quantitatively and detects the therapeutic indications for drug combinations. Finally, the PreDC database includes 1571 known and 3269 predicted optimal combinations as well as their potential side effects and therapeutic indications.

AVAILABILITY AND IMPLEMENTATION

The PreDC database is available at http://sm.nwsuaf.edu.cn/lsp/predc.php.

Evaluation of 4β-Hydroxycholesterol as a Clinical Biomarker of CYP3A4 Drug Interactions Using a Bayesian Mechanism-Based Pharmacometric Model

A Bayesian mechanism–based pharmacokinetic/pharmacodynamic model of cytochrome P450 3A4 (CYP3A4) activity was developed based on a clinical study of the effects of ketoconazole and rifampin on midazolam exposure and plasma 4β-hydroxycholesterol (4βHC) concentrations. Simulations from the model demonstrated that the dynamic range of 4βHC as a biomarker of CYP3A4 induction or inhibition was narrower than that of midazolam; an inhibitor that increases midazolam area under the curve by 20-fold may only result in a 20% decrease in 4βHC after 14 days of dosing. Likewise, an inducer that elevates CYP3A4 activity by 1.2-fold would reduce the area under the curve of midazolam by 50% but would only increase 4βHC levels by 20% after 14 days of dosing. Elevation in 4βHC could be reliably detected with a twofold induction in CYP3A4 activity with study sample sizes (N ~ 6–20) typically used in early clinical development. Only a strong CYP3A4 inhibitor (e.g., ketoconazole) could be detected with similar sample sizes.

Text mining for drug-drug interaction

In order to understand the mechanisms of drug-drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are significant. In recent years, drug PK parameters, drug interaction parameters, and PG data have been unevenly collected in different databases and published extensively in literature. Also the lack of an appropriate PK ontology and a well-annotated PK corpus, which provide the background knowledge and the criteria of determining DDI, respectively, lead to the difficulty of developing DDI text mining tools for PK data collection from the literature and data integration from multiple databases.To conquer the issues, we constructed a comprehensive pharmacokinetics ontology. It includes all aspects of in vitro pharmacokinetics experiments, in vivo pharmacokinetics studies, as well as drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three-level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK corpus was demonstrated by a drug interaction extraction text mining analysis.The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions.

Evaluation of utility of pharmacokinetic studies in phase I trials of two oncology drugs

PURPOSE

There are many phase I trials of oncology drug combinations, very few of which report clinically significant pharmacokinetic interactions. We hypothesized that the utility of such pharmacokinetic drug-drug interaction (DDI) studies is low in the absence of a mechanistic hypothesis.

EXPERIMENTAL DESIGN

We retrospectively reviewed 152 phase I (two drug) combination studies published between 2007 and 2011.

RESULTS

Only 28 (18%) studies had an implicit or explicit rationale, either inhibition/induction of a drug-metabolizing enzyme or transporter, cosubstrates for the same enzyme or transporter, potential for end-organ toxicity, or protein binding. Only 12 (8%) studies demonstrated a statistically significant DDI, on the basis of change in clearance (or area under the curve) of parent drug and/or active metabolite. There was a strong association between a rationale and a demonstrable drug interaction, as only 2% of studies without a rationale demonstrated a DDI, compared with 32% of studies with a rationale (Fisher exact test; P < 10(-6)).

CONCLUSION

DDI studies should not be routinely performed as part of phase I trials of oncology combinations.

Informatics confronts drug-drug interactions

Drug-drug interactions (DDIs) are an emerging threat to public health. Recent estimates indicate that DDIs cause nearly 74000 emergency room visits and 195000 hospitalizations each year in the USA. Current approaches to DDI discovery, which include Phase IV clinical trials and post-marketing surveillance, are insufficient for detecting many DDIs and do not alert the public to potentially dangerous DDIs before a drug enters the market. Recent work has applied state-of-the-art computational and statistical methods to the problem of DDIs. Here we review recent developments that encompass a range of informatics approaches in this domain, from the construction of databases for efficient searching of known DDIs to the prediction of novel DDIs based on data from electronic medical records, adverse event reports, scientific abstracts, and other sources. We also explore why DDIs are so difficult to detect and what the future holds for informatics-based approaches to DDI discovery.

Drug interactions--principles, examples and clinical consequences

BACKGROUND

Drug interactions can have desired, reduced or unwanted effects. The probability of interactions increases with the number of drugs taken. The high rate of prescribed drugs in elderly patients (65-year-old patients take an average of 5 drugs) increases the likelihood of drug interactions and thus the risk that drugs themselves can be the cause of hospitalization. According to meta-analyses, up to 7% of hospitalizations are drug-related.

METHODS

Selective literature review.

RESULTS

Drug interactions occur on pharmacodynamic and pharmacokinetic levels. Examples of pharmacodynamic interactions are simultaneous administration of a NSAID and phenprocoumon (additive interaction), or of aspirin and ibuprofen (antagonistic interaction). Pharmacokinetic interactions occur at the levels of absorption (e.g., levothyroxine and neutralizing antacids), elimination (e.g., digoxin and macrolides), and metabolism, as in the competition for cytochrome P450 enzymes (e.g., SSRIs and certain beta-blockers).

CONCLUSION

The systematic knowledge of drug interaction, in particular on the level of absorption, elimination, transport and drug metabolism may help to prevent adverse effects. Predicting pharmacodynamic interactions often demands a deeper understanding of the mechanisms of effect. Electronic prescribing systems are helpful.

INDI: a computational framework for inferring drug interactions and their associated recommendations

INDI is a similarity-based drug–drug interaction prediction method that can infer both pharmacokinetic and pharmacodynamic interactions, as well as their severity levels. Both known and predicted drug interactions are found to be prevalent in clinical practice.

INDI is a similarity-based drug–drug interaction prediction method, capable of handling both pharmacokinetic and pharmacodynamic interactions.

INDI predicts the severity of the interaction and the Cytochrome P450 isozyme involved in pharmacokinetic interactions.

We show the prevalence of known and predicted drug interactions in drug adverse reports and in chronic medications taken by hospitalized patients.

Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP-related DDIs (along with their associated CYPs) and pharmacodynamic, non-CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver-operating characteristic curve)⩾0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co-administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/∼bnet/software/INDI, facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike.

A novel global search algorithm for nonlinear mixed-effects models using particle swarm optimization

NONMEM is one of the most popular approaches to a population pharmacokinetics/pharmacodynamics (PK/PD) analysis in fitting nonlinear mixed-effects models. As a local optimization algorithm, NONMEM usually requires an initial value close enough to the global optimum. This paper proposes a novel global search algorithm called P-NONMEM. It combines the global search strategy by particle swarm optimization (PSO) and the local estimation strategy of NONMEM. In the proposed algorithm, initial values (particles) are generated randomly by PSO, and NONMEM is implemented for each particle to find a local optimum for fixed effects and variance parameters. P-NONMEM guarantees the global optimization for fixed effects and variance parameters. Under certain regularity conditions, it also leads to global optimization for random effects. Because P-NONMEM doesn't run PSO search for random effect estimation, it avoids tremendous computational burden. In the simulation studies, we have shown that P-NONMEM has much improved convergence performance than NONMEM. Even when the initial values were far away from the global optima, P-NONMEM converged nicely for all fixed effects, random effects, and variance components.

Non-compartment model to compartment model pharmacokinetics transformation meta-analysis--a multivariate nonlinear mixed model

BACKGROUND

To fulfill the model based drug development, the very first step is usually a model establishment from published literatures. Pharmacokinetics model is the central piece of model based drug development. This paper proposed an important approach to transform published non-compartment model pharmacokinetics (PK) parameters into compartment model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed model. A conditional first-order linearization approach was developed for statistical estimation and inference.

RESULTS

Using MDZ as an example, we showed that this approach successfully transformed 6 non-compartment model PK parameters from 10 publications into 5 compartment model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed model had little relative bias (<1%) in estimating compartment model PK parameters if all non-compartment PK parameters were reported in every study. If there missing non-compartment PK parameters existed in some published literatures, the relative bias of compartment model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%.

CONCLUSIONS

This non-compartment model PK parameter transformation into compartment model meta-analysis approach possesses valid statistical inference. It can be routinely used for model based drug development.

A novel Gibbs maximum a posteriori (GMAP) approach on Bayesian nonlinear mixed-effects population pharmacokinetics (PK) models

In this paper, various Bayesian Monte Carlo Markov chain (MCMC) methods and the proposed algorithm, the Gibbs maximum a posteriori (GMAP) algorithm, are compared for implementing the nonlinear mixed-effects model in pharmacokinetics (PK) studies. An intravenous two-compartmental PK model is adopted to fit the PK data from the midazolam (MDZ) studies, which recruited twenty-four individuals with nine different time points per subject. The three-stage hierarchical nonlinear mixed model is constructed. Data analysis and model performance comparisons show that GMAP converges the fastest and provides reliable results. At the mean time, data augmentation (DA) methods are used for the Random-walk Metropolis method. Data analysis shows that the speed of the convergence of Random-walk Metropolis can be improved by DA, but all of them are not as fast as GMAP. The performance of GMAP and various MCMC algorithms are compared through Midazolam data analysis and simulation.

Drug-drug interaction prediction assessment

Model-based drug-drug interaction (DDI) is an important in-silico tool to assess the in vivo consequences of in vitro DDI. Before its general application to new drug compounds, the DDI model is always established from known interaction data. For the first time, tests for difference and equivalent tests are implemented to compare reported and model-base simulated DDI (log AUCR) in the sample mean and variance. The biases and predictive confidence interval coverage probabilities are introduced to assess the DDI prediction performance. Sample size and power guidelines are developed for DDI model simulations. These issues have never been discussed in trial simulation studies to investigate DDI prediction. A ketoconazole (KETO)/midazolam (MDZ) example is employed to demonstrate these statistical methods. Based on published KETO and MDZ pharmacokinetics data and their in vitro inhibition rate constant data, current model-based DDI prediction underpredicts the area under concentration curve ratio (AUCR) and its between-subject variance compared to the reported study.

A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction

In drug-drug interaction (DDI) research, a two-drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject-specific drug concentration data from clinical PK studies on inhibitor or inducer and substrate PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. Hence there is a great need for meta-analysis and DDI prediction using such summarized PK data. In this study, an innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The three levels model sample means and variances, between-study variances, and prior distributions. Through a ketoconazle-midazolam example and simulations, we demonstrate that our meta-analysis model can not only estimate PK parameters with small bias but also recover their between-study and between-subject variances well. More importantly, the posterior distributions of PK parameters and their variance components allow us to predict DDI at both population-average and study-specific levels. We are also able to predict the DDI between-subject/study variance. These statistical predictions have never been investigated in DDI research. Our simulation studies show that our meta-analysis approach has small bias in PK parameter estimates and DDI predictions. Sensitivity analysis was conducted to investigate the influences of interaction PK parameters, such as the inhibition constant Ki, on the DDI prediction.

A new probabilistic rule for drug-dug interaction prediction

An innovative probabilistic rule is proposed to predict the clinical significance or clinical insignificance of DDI. This rule is coupled with a hierarchical Bayesian model approach to summarize substrate/inhibitor's PK models from multiple published resources. This approach incorporates between-subject and between-study variances into DDI prediction. Hence, it can predict both population-average and subject-specific AUCR. The clinically significant DDI, weak DDI, and clinically insignificant inhibitions are decided by the probabilities of predicted AUCR falling into three intervals, (-infinity, 1.25), (1.25, 2), and (2, infinity). The main advantage of this probabilistic rule to predict clinical significance of DDI over the deterministic rule is that the probabilistic rule considers the sample variability, and the decision is independent of sampling variation; while deterministic rule based decision will vary from sample to sample. The probabilistic rule proposed in this paper is best suited for the situation when in vivo PK studies and models are available for both the inhibitor and substrate. An early decision on clinically significant or clinically insignificant inhibition can avoid additional DDI studies. Ketoconazole and midazolam are used as an interaction pair to illustrate our idea. AUCR predictions incorporating between-subject variability always have greater variances than population-average AUCR predictions. A clinically insignificant AUCR at population-average level is not necessarily true when considering between-subject variability. Additional simulation studies suggest that predicted AUCRs highly depend on the interaction constant K(i) and dose combinations.