part of the reason for this is that in clinical trials, men vastly outnumber women. and the data — reflective of the male experience — is used by doctors and patients to make decisions about prescribing.
it’s why chandak and a team of researchers recently developed an algorithm that can effectively predict how adverse reactions will affect both sexes. awaredx — which stands for analysing women at risk for experiencing drug toxicity — a machine learning algorithm that identifies and predicts differences in adverse drug effects between men and women, works by analyzing fda adverse reaction reports from the past 50 years. it corrects this gender bias, levelling the playing field when it comes to this type of medication reporting. the findings are reported in cell press.
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it also means that the safety profiles of drugs may not be reliable for women who often have very different reactions to medications than men. for example, certain sleep aids like ambien are metabolized at a much slower rate in women. women also tend to have a lower body weight, smaller organ size and a higher percentage of body fat, which affects the absorption of drugs. and women are more likely to take drugs concurrently. these differences can have a big impact on women’s quality of life and health, say the authors.
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