Drug–drug interactions and their implications on the pharmacokinetics of the drugs

Polaka, Suryanarayana; Koppisetti, Hari Priya; Tekade, Muktika; Sharma, Mukesh C.; Sengupta, Pinaki; Tekade, Rakesh K.

doi:10.1016/b978-0-323-98367-9.00007-x

Cited by 6 publications

(1 citation statement)

References 109 publications

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“…This can result in altered drug levels or unexpected side effects. 10 Sex/gender-specific pharmacology guidelines are scarce, 11 except for adjustments for body weight or body surface area and pregnancy concerns. There are no currently commonly accepted dose recommendations for approved drugs in Europe or North America based on pharmacokinetic sex differences.…”

Section: Introductionmentioning

confidence: 99%

Sex Differences in Cardiovascular Management: A Call for Better Acknowledgment—Part 1 Pharmacological Differences in Women and Men; How Relevant Are They?

Ivanescu,

Dan

2024

American Journal of Therapeutics

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Background: Sex differences (SDs) in pharmacology of cardiovascular (CV) drugs have been described previously; however, paradoxically, there are scarce recommendations in therapy based on these differences. It is of utmost importance to identify whether these SDs determine a modified clinical response and the potential practical implications for this, to provide a base for personalized medicine. Area of uncertainty: The aim of this article was to outline the most important pharmacological drivers of cardiovascular drugs that differ between women and men, along with their implications and challenges in clinical practice. Data sources: A detailed assessment of English-written resources reflecting SDs impact in CV drug pharmacology was performed using PubMed and Embase databases. Results: Despite large variations in CV drug pharmacokinetics and pharmacodynamics in individuals, correcting for height, weight, surface area, and body composition compensate for most “sex-dependent” differences. In addition, individual, cultural, and social factors significantly impact disease management in women versus men. Gender-biased prescribing patterns and gender-dependent adherence to therapy also influence outcomes. The development of sex-specific guidelines requires that they should reflect the SDs implications for the management of a disease and that the evidence should be carefully evaluated as to whether there is an adequate representation of both sexes and whether sex-disaggregated data are reported. Conclusions: Pharmacological drivers are under the influence of an impressive number of differences between women and men. However, to establish their significance in clinical practice, an adequate representation of women in studies and the reporting of distinct results is mandatory.

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Section: Introductionmentioning

confidence: 99%

Sex Differences in Cardiovascular Management: A Call for Better Acknowledgment—Part 1 Pharmacological Differences in Women and Men; How Relevant Are They?

Ivanescu,

Dan

2024

American Journal of Therapeutics

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Timing Matters: A Machine Learning Method for the Prioritization of Drug–Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure

Battini,

Cocco,

Barbieri

et al. 2024

Drug Saf

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Introduction Current drug–drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive disproportionality signals in spontaneous reporting system (SRS) databases. Objective This study aims to develop a method for detecting and prioritizing temporally plausible disproportionality signals of DDIs in SRS databases by incorporating co-exposure time in disproportionality analysis. Methods The method was tested in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The CRESCENDDI dataset of positive controls served as the primary source of true-positive DDIs. Disproportionality analysis was performed considering the time of co-exposure. Temporal plausibility was assessed using the flex point of cumulative reporting of disproportionality signals. Potential confounders were identified using a machine learning method (i.e. Lasso regression). Results Disproportionality analysis was conducted on 122 triplets with more than three cases, resulting in the prioritization of 61 disproportionality signals (50.0%) involving 13 adverse events, with 61.5% of these included in the European Medicine Agency’s (EMA’s) Important Medical Event (IME) list. A total of 27 signals (44.3%) had at least ten cases reporting the triplet of interest, and most of them ( n = 19; 70.4%) were temporally plausible. The retrieved confounders were mainly other concomitant drugs. Conclusions Our method was able to prioritize disproportionality signals with temporal plausibility. This finding suggests a potential for our method in pinpointing signals that are more likely to be furtherly validated. Supplementary Information The online version contains supplementary material available at 10.1007/s40264-024-01430-8.

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Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Yadav,

Maldonato,

Roesner

et al. 2024

Drug Metabolism Reviews

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Drug–drug interactions and their implications on the pharmacokinetics of the drugs

Cited by 6 publications

References 109 publications

Sex Differences in Cardiovascular Management: A Call for Better Acknowledgment—Part 1 Pharmacological Differences in Women and Men; How Relevant Are They?

Sex Differences in Cardiovascular Management: A Call for Better Acknowledgment—Part 1 Pharmacological Differences in Women and Men; How Relevant Are They?

Timing Matters: A Machine Learning Method for the Prioritization of Drug–Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

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