Drug combinations screening using a Bayesian ranking approach based on dose-response models

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.

Occurrence and severity of cocaine-induced hallucinations: Two distinct phenotypes with shared clinical factors but specific genetic risk factors

Cocaine-induced transient hallucinations (CIH) are a frequent complication following cocaine intake that is associated with addiction severity.

METHODS

Two hundred and forty-two non-psychotic and Caucasian lifetime cocaine users were included in a French multicentric study. Clinical variables and dopamine pathway genotype data were extracted and tested with CIH scores using a zero-inflated binomial model, which allows for the exploration of factors associated with occurrence and severity separately.

RESULTS

Cocaine dependence (p_occurrence= 6.18 × 10^-5, p_severity= 9.25 × 10^-8), number of cocaine dependence DSM IV-Tr criteria (p_occurrence= 1.22 × 10^-7, p_severity= 5.09 × 10^-6), and frequency of intake during the worst period of misuse (p_occurrence= 8.51 × 10^-04, p_severity= 0.04) were associated with greater occurrence and higher severity of CIH. The genetic associations did not yield significant results after correction for multiple tests. However, some nominal associations of SNPs mapped to the VMAT2, DBH, DRD1, and DRD2 genes were significant. In the multivariate model, the significant variables were the number of cocaine dependence criteria, lifetime alcohol dependence, and the nominally associated SNPs.

CONCLUSION

Our study shows that CIH occurrence and severity are two distinct phenotypes, with shared clinical risk factors; however, they likely do not share the same genetic background.