How precise is quantitative prediction of pharmacokinetic effects due to drug-drug interactions and genotype from in vitro data? A comprehensive analysis on the example CYP2D6 and CYP2C19 substrates

A weighted Bayesian integration method for predicting drug combination using heterogeneous data

BACKGROUND

In the management of complex diseases, the strategic adoption of combination therapy has gained considerable prominence. Combination therapy not only holds the potential to enhance treatment efficacy but also to alleviate the side effects caused by excessive use of a single drug. Presently, the exploration of combination therapy encounters significant challenges due to the vast spectrum of potential drug combinations, necessitating the development of efficient screening strategies.

METHODS

In this study, we propose a prediction scoring method that integrates heterogeneous data using a weighted Bayesian method for drug combination prediction. Heterogeneous data refers to different types of data related to drugs, such as chemical, pharmacological, and target profiles. By constructing a multiplex drug similarity network, we formulate new features for drug pairs and propose a novel Bayesian-based integration scheme with the introduction of weights to integrate information from various sources. This method yields support strength scores for drug combinations to assess their potential effectiveness.

RESULTS

Upon comprehensive comparison with other methods, our method shows superior performance across multiple metrics, including the Area Under the Receiver Operating Characteristic Curve, accuracy, precision, and recall. Furthermore, literature validation shows that many top-ranked drug combinations based on the support strength score, such as goserelin and letrozole, have been experimentally or clinically validated for their effectiveness.

CONCLUSIONS

Our findings have significant clinical and practical implications. This new method enhances the performance of drug combination predictions, enabling effective pre-screening for trials and, thereby, benefiting clinical treatments. Future research should focus on developing new methods for application in various scenarios and for integrating diverse data sources.

Combined and independent effects of OCT1 and CYP2D6 on the cellular disposition of drugs

The organic cation transporter 1 (OCT1) mediates the cell uptake and cytochrome P450 2D6 (CYP2D6) the metabolism of many cationic substrates. Activities of OCT1 and CYP2D6 are affected by enormous genetic variation and frequent drug-drug interactions. Single or combined deficiency of OCT1 and CYP2D6 might result in dramatic differences in systemic exposure, adverse drug reactions, and efficacy. Thus, one should know what drugs are affected to what extent by OCT1, CYP2D6 or both. Here, we compiled all data on CYP2D6 and OCT1 drug substrates. Among 246 CYP2D6 substrates and 132 OCT1 substrates, we identified 31 shared substrates. In OCT1 and CYP2D6 single and double-transfected cells, we studied which, OCT1 or CYP2D6, is more critical for a given drug and whether there are additive, antagonistic or synergistic effects. In general, OCT1 substrates were more hydrophilic than CYP2D6 substrates and smaller in size. Inhibition studies showed unexpectedly pronounced inhibition of substrate depletion by shared OCT1/CYP2D6 inhibitors. In conclusion, there is a distinct overlap in the OCT1/CYP2D6 substrate and inhibitor spectra, so in vivo pharmacokinetics and -dynamics of shared substrates may be significantly affected by frequent OCT1- and CYP2D6-polymorphisms and by comedication with shared inhibitors.

Characterisation of seven medications approved for attention-deficit/hyperactivity disorder using in vitro models of hepatic metabolism

The metabolism of most medications approved for the treatment of attention deficit/hyperactivity disorder (ADHD) is not fully understood.In vitro studies using cryopreserved, plated human hepatocytes (cPHHs) and pooled human liver microsomes (HLMs) were performed to more thoroughly characterise the metabolism of several ADHD medications.The use of enzyme-specific chemical inhibitors indicated a role for CYP2D6 in atomoxetine (ATX) metabolism, and roles for CYP3A4/5 in guanfacine (GUA) metabolism.The 4-hydroxy-atomoxetine and N-desmethyl-atomoxetine pathways represented 98.4% and 1.5% of ATX metabolism in cPHHs, respectively. The 3-OH-guanfacine pathway represented at least 2.6% of GUA metabolism in cPHHs, and 71% in HLMs.The major metabolising enzyme for methylphenidate (MPH) and dexmethylphenidate (dMPH) could not be identified using these methods because these compounds were too unstable. Hydrolysis of these medications was spontaneous and did not require the presence of protein to occur.Clonidine (CLD), amphetamine (AMPH), and dextroamphetamine (dAMPH) did not deplete substantially in cPHHs nor HLMs, suggesting that these compounds may not undergo considerable hepatic metabolism. The major circulating metabolites of AMPH and dAMPH (benzoic acid and hippuric acid) were not observed in either system, and therefore could not be characterised. Additionally, inhibition experiments suggested a very minimal role for CYP2D6 in CLD and AMPH metabolism.