Drug discovery’s inefficient now due to cautious regulation, increasing costs, and reliance on trial-and-error. AI revolutionizes testing, eliminating the risk of human error, which is key to rapid drug rollout.

Rubstein 23, [Adrian Rubstein (CEO of Ax3.Bio, Bioentrepreneur, Biotechnology Advisor), “Redefining drug development: the trailblazing evolution of the pharmaceutical industry,” Labiotech, , accessed 7-4-2024, 11/29/23] Mr.Mr.

For any pharmaceutical company or academic institution, it is a big achievement to advance a drug candidate to phase 1 clinical trials after drug candidates are rigorously optimized at the preclinical stage. However, nine out of ten drug candidates would fail during phase 1, 2, 3 clinical trials and drug approval.

It is also worth noting that the 90% failure rate is for the drug candidates that are already advanced to phase 1 clinical trials, which does not include the drug candidates in the preclinical stages. If drug candidates in the preclinical stage are also counted, the failure rate of drug discovery/development is even higher than 90%. The concept we use for this low efficiency in drug development is called Eroom´s Law.

Why does drug discovery and development fail?

In chapter 21 of Artificial intelligence and machine learning in clinical trial design and application, Robert J. Beetel and fellow authors explained: “There are many explanations given for why drug discovery has followed Eroom’s law, from cautious regulators to increasing overall R&D costs. But one of the biggest areas holding back progress is inefficiency in the drug discovery phase, the preclinical animal testing and clinical trial phase of the drug-discovery process.”

What percentage of these compounds contain drug-like properties that may be utilized to produce life-saving drugs? Millions? Billions? Trillions? The answer is 1060 novemdecillion. To put this into context, the Milky Way contains around 100 billion, or 1011, stars.

From phase 1 to phase 3, the typical drug development process depended on serendipity, experimentation, optimization, animal models, and evaluating the new medicine in people. Various technologies contested the key in an attempt to disrupt this process. This article will attempt to demonstrate why three major technologies currently being developed and tested have the potential to change the curse of drug development: artificial intelligence (AI) for drug discovery, organ-on-a-chip (OOC) for testing without animal models, and digital twins for clinical trials.

This is not science fiction, but rather a plausible evolution in drug discovery that might become the norm. Let’s get granular on how these new technologies can disrupt drug discovery.

Drug discovery evolution: the use of generative AI

Drug discovery is an intricate, trial-and-error process. But then, AI intervened, revolutionizing the landscape.

AI, showcased by platforms like DALL-E 2 and Midjourney, transitioned from generating surreal images to redefining drug development. It is infiltrating every stage of the process, from identification to compound design and predictions. Nvidia’s BioNemo emerged as a comprehensive tool, amalgamating diverse AI-driven functions like 3D protein structure prediction, protein property predictions, small molecule generation or protein generation, and molecular docking.

Nevertheless, there are many companies with proprietary platforms and databases working on their own pipeline or partnering with pharma, just to mention a few: Insitro, Insilico Medicine, AtomWise, Healx, Verseon and Qubit (Quantum technology for drug discovery).

The revelation of AI’s potential came with a BCG and Wellcome report, projecting significant time and cost savings by about 25–50%. Subsequently, success stories emerged: Exscientia led the way, producing groundbreaking molecules for clinical trials with AI’s aid (A2A receptor antagonist for solid tumors and a selective serotonin reuptake inhibitor for obsessive-compulsive disorder). Insilico Medicine followed suit, launching a phase 1 trial for an AI-designed senolytic drug targeting aging-related diseases.

And many other examples followed, like Relay Therapeutics with its RLAY-4008 for cholangiocarcinoma, Nimbus Therapeutics with NDI-034858 for moderate to severe psoriasis, and Evaxion with EVX-01 for melanoma. In these 3 cases, the use of AI decreased the time between discovery to clinical trials.

AI’s impact is seismic, blurring the lines between human ingenuity and machine precision. It isn’t merely about molecules but the fusion of human and machine intelligence, charting a new course in pharmaceutical discovery.

AI isn´t just disrupting the industry; it is catalyzing evolution in drug discovery, paving the way for a future where technology and human endeavor coalesce, propelling science toward innovative breakthroughs and healing. Think about making a 4-year process in about a couple of weeks. It’s mind-blowing to think how far we can get if you think about vertical integration between startups (experts on innovation) and big pharmaceutical companies (experts in clinical trial, regulatory, and commercialization) joining forces.