Individualized Drugs' Selection by Evaluation of Drug Properties, Pharmacogenomics and Clinical Parameters: Performance of a Bioinformatic Tool Compared to a Clinically Established Counselling Process

Opinion paper on the systematic application of integrated bioinformatic tools to actuate routine precision medicine in poly-treated patients

Precision Medicine is a reality in selected medical areas, as oncology, or in excellent healthcare structures, but it is still far to reach million patients who could benefit from this medical concept. Here, we sought to highlight how the time is ripe to achieve horizontal delivery to a significant larger audience of patients, represented by the poly-treated patients. Combination therapies are frequent (especially in the elderly, to treat comorbidities) and are related to decreased drug safety and efficacy, disease's exacerbation, additional treatments, hospitalization. But the recent development and validation of bioinformatic tools, aimed to automatic evaluation and optimization of poly-therapies, according to the unique individual characteristics (including genotype), is ready to change the daily approach to pharmacological prescription.

Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.

Diazepam diminishes temozolomide efficacy in the treatment of U87 glioblastoma cell line

AIMS

Many patients with glioblastoma (GBM) suffer from comorbid neurological/psychiatric disorders and, therefore, are treated with psychopharmacological agents. Diazepam (DIA) is widely adopted to treat status epilepticus, alleviate anxiety, and inhibit chemotherapy-associated delayed emesis in GBM patients. Even though temozolomide (TMZ) and DIA could be found as possible combination therapy in clinical practice, there are no reports of their combined effects in GBM. Hence, it may be of interest to investigate whether DIA enhances the antitumor efficacy of TMZ in GBM cells.

METHODS

U87 human GBM was used to examine the effects of combined TMZ and DIA on cell viability, and the oxygen consumption within the cells, in order to evaluate mitochondrial bioenergetic response upon the treatment.

RESULTS

The cooperative index showed the presence of antagonism between TMZ and DIA, which was confirmed on long-term observation. Moreover, the level of apoptosis after the TMZ treatment was significantly decreased when administered with DIA (p < 0.001). Concomitant use of TMZ and DIA increased the basal cell respiration rate, the oxidative phosphorylation rate, and maximal capacity of mitochondrial electron transport chain, as well as the activities of complexes I and II, vs. TMZ alone (p < 0.001).

CONCLUSION

Comparing our results with data reported that DIA elicits cell cycle arrest in the G0/G1 phase and favors senescence reveals that DIA diminishes TMZ efficacy in concomitant use in the treatment of GBM. However, due to its great potency to hinder GBM proliferation and metabolism, it could be considered using DIA as maintenance therapy after TMZ cycles.