Pharmacogenetic and safety analysis of cinacalcet hydrochloride in healthy Chinese subjects

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study

BACKGROUND

Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates.

OBJECTIVE

In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety.

METHODS

In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades.

RESULTS

The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages.

CONCLUSIONS

In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.

Label-Free Mass Spectrometry-Based Quantitative Proteomics to Evaluate the Effects of the Calcium-Sensing Receptor Agonist Cinacalcet on Protein Expression in Rat Brains and Livers

Background

Cinacalcet is a calcium-sensing receptor agonist that is clinically approved for the treatment of secondary hyperparathyroidism in chronic kidney disease and hypercalcemia in patients with parathyroid carcinoma. This study aimed to use quantitative mass spectrometry-based label-free proteomics to evaluate the effects of cinacalcet on protein expression in rat brains and livers.

Material/Methods

We randomly assigned 18 Wistar rats to 2 groups: an untreated control group (n=6) and a group treated with cinacalcet at a dose corresponding to the maximum dose used in humans (2 mg/kg/body weight, 5 days/week) divided into 7-day (n=6) and 21-day (n=6) treatment subgroups. A mass-spectrometry-based label-free quantitative proteomics approach using peptides peak area calculation was used to evaluate the changes in protein expression in examined tissues. Bioinformatics analysis of quantitative proteomics data was done using MaxQuant and Perseus environment.

Results

No changes in protein expression were revealed in the 7-day treatment subgroup. We detected 10 upregulated and 3 downregulated proteins in the liver and 1 upregulated protein in the brain in the 21-day treatment subgroup compared to the control group. Based on Gene Ontology classification, all identified differentially expressed proteins were indicated as molecular functions involved in the enzyme regulator activity (36%), binding (31%), and catalytic activity (19%).

Conclusions

These findings indicate that long-term cinacalcet therapy can impair phase II of enzymatic detoxication and can cause disturbances in blood hemostasis, lipid metabolism, and inflammatory mediators or contribute to the acceleration of cognitive dysfunction; therefore, appropriate patient monitoring should be considered.