A new Nature Neuroscience commentary warns that phasing out animal research before female biology is well understood could lock historical male bias into the next generation of health innovation.
This is indeed a blind spot. Males and females respond differently to already approved drugs, and sex is often neglected as a biological variable when developing new therapeutics. In medicine, females often have different presentations (i.e. myocardial infarction) than males. Unfortunately, it is often easier (cheaper) to run preclinical efficacy studies in males only - and this is routine. Most scientists acknowledge the need to include sex as a variable in both mechanistic biology and drug discovery, however financial resources are often the limiting factor.
This is a blind spot that we are purposely avoiding. Would love to chat more around this topic. I had such hope for AI, but see in my own care how it can fail us.
I think that’s the part many people still underestimate. AI is often framed as objective because the outputs feel computational, but medicine is never starting from neutral ground. If the underlying biological and clinical assumptions are incomplete, the systems built on top of them can scale those gaps very efficiently.
This is indeed a blind spot. Males and females respond differently to already approved drugs, and sex is often neglected as a biological variable when developing new therapeutics. In medicine, females often have different presentations (i.e. myocardial infarction) than males. Unfortunately, it is often easier (cheaper) to run preclinical efficacy studies in males only - and this is routine. Most scientists acknowledge the need to include sex as a variable in both mechanistic biology and drug discovery, however financial resources are often the limiting factor.
Would love to discuss this more with you.
This is a blind spot that we are purposely avoiding. Would love to chat more around this topic. I had such hope for AI, but see in my own care how it can fail us.
I think that’s the part many people still underestimate. AI is often framed as objective because the outputs feel computational, but medicine is never starting from neutral ground. If the underlying biological and clinical assumptions are incomplete, the systems built on top of them can scale those gaps very efficiently.
would love to chat more too Christine. I'll send you a DM