Dr Peter Attia is a bestselling writer and his outstanding communication skills mean that laypeople like us can understand complex medical developments without having to strain whatever few grey cells we have. In this blog, he explains how AI can bring down the cost of bringing a new drug to the market (a process which is widely reported to cost a staggering $1bn). If Dr Attia is right and if drug development costs indeed fall, then that’s not just good news for humanity (imagine the new remedies that the Pharma industry will then offer us) but also for Indian pharma companies (who simply can’t afford to spend $1bn per drug). To understand how this might happen we would strongly recommend that you read Dr Attia’s article in full because it contains excellent tables and charts which contain much of the argument.
First off, Dr Attia explains to us the 4 stages of the drug development process notably: “In the earliest stages, candidate drugs undergo years of testing and development to evaluate efficacy and potential toxicity through in vitro cell culture and in vivo animal studies. Preclinical animal studies are also used to determine initial human dosing, scaled by relative differences in weight.
Before testing can proceed from animals to humans, the drug’s sponsor (often the manufacturer) must then submit an investigational new drug (IND) application. This application includes the preclinical efficacy and toxicology data, detailed protocols for the initial clinical trial phases, and manufacturing information meeting the requirements for “Good Manufacturing Practices” (GMP) – criteria that establish, for instance, a manufacturer’s ability to consistently supply a drug for clinical trials with the necessary quality controls. The FDA reviews IND applications to gauge the risks to research study participants and rejects the application if risk is deemed too high or if there are study design flaws. Once an IND application is approved, the clinical trial process can begin…”
Dr Attia then explains where most drugs struggle to make it through the 4 stage process: “Most drug candidates – roughly 90% – fail at some point between preclinical studies and the conclusion of a phase III trial…
In all, drugs failing to show an expected therapeutic effect account for the largest percentage of failures in drug development – between 40-50%. Biological discrepancies between cultured cell lines, animal models, and humans make it difficult to validate if a drug is successfully reaching and affecting its intended molecular target. Further, in order for a drug to receive approval, the FDA must deem efficacy to be not only statistically significant, but clinically significant as well. Study outcomes are usually set before the initiation of a phase III trial by the drug company, not the FDA or a third party, and a “successful” study outcome doesn’t automatically mean an outcome with true clinical value. This is also why approved uses are limited to the studied population. An antidiabetic drug that improves glycemic control and induces significant weight loss, isn’t approved for on-label use in treating obesity until an obesity-specific trial is run.
Another 30% of failures are attributed to toxicity, either due to off-target effects or the accumulation of the drug in human tissues.”
Enter the white knight of our era, AI. Dr Attia writes: “The most impactful advancements will likely arise from the use of artificial intelligence (AI) and machine learning, which are already substantially accelerating all stages of the drug development process. AI algorithms can sift through large datasets (e.g., “omics” data) to identify novel drug targets or genes and even aid in drug design by predicting the three-dimensional structure of the target molecule. Once a target is identified, AI can screen large libraries of molecules for potential drug candidates as well as generate de novo drug designs. Such an approach might also identify new potential indications for existing drugs, which, as discussed in a recent newsletter, can drastically accelerate the path from concept to approval. Further, AI simulations can be used to predict drug activity and safety, reducing the number of necessary animal studies. The widespread use of AI in drug development has the potential to both lower the cost of bringing a drug to market and reduce the development time. The lower costs of development and AI-enabled drug discovery will potentially incentivize companies to devote more time and resources to developing therapeutics for rare diseases since currently, the high cost of development outweighs any return on investment from a limited market…
The drug development process has advanced considerably over the last century to enhance rigor. Yet the process is also extraordinarily costly and time-consuming, with plenty of room for improvement with better study designs and the integration of AI.”
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