New In Vitro-In Silico Approach for the Prediction of In Vivo Performance of Drug Combinations

Pharmacokinetic (PK) studies improve the design of dosing regimens in preclinical and clinical settings. In complex diseases like cancer, single-agent approaches are often insufficient for an effective treatment, and drug combination therapies can be implemented. In this work, in silico PK models were developed based on in vitro assays results, with the goal of predicting the in vivo performance of drug combinations in the context of cancer therapy. Combinations of reference drugs for cancer treatment, gemcitabine and 5-fluorouracil (5-FU), and repurposed drugs itraconazole, verapamil or tacrine, were evaluated in vitro. Then, two-compartment PK models were developed based on the previous in vitro studies and on the PK profile reported in the literature for human patients. Considering the quantification parameter area under the dose-response-time curve (AUC(effect)) for the combinations effect, itraconazole was the most effective in combination with either reference anticancer drugs. In addition, cell growth inhibition was itraconazole-dose dependent and an increase in effect was predicted if itraconazole administration was continued (24-h dosing interval). This work demonstrates that in silico methods and AUC(effect) are powerful tools to study relationships between tissue drug concentration and the percentage of cell growth inhibition over time.

Automated summary

PK studies are important for dosing regimen design. Combination therapies may be more effective in cancer treatment. In this study, in silico models were used to predict the in vivo performance of drug combinations in cancer therapy, with itraconazole showing the most promising results in combination with reference anticancer drugs.