AI Isn’t the Magic Bullet to Simplify Drug Discovery

“Wet-lab patient testing and translational models are key to confirming AI-derived finding,” OncoDxRx said. End Quote

(1888PressRelease) June 10, 2024 - Artificial intelligence (AI) is big business in the pharmaceutical industry. According to Deep Pharma Intelligence, cumulative investments in AI-related drug development between 2014 and 2023 topped $60 billion. With the recent $1 billion launch of Xaira Therapeutics, and seemingly endless new deals between various pharmaceutical companies and NVIDIA, $60 billion seems to be a low projection for 2024 spending.

Alongside the PR hype, however, there are real conversations taking place among industry leaders questioning whether the money flowing into AI will meaningfully improve productivity and output in the industry. While biotechnology companies have been touting a decade-old narrative about AI drug discovery being exponentially faster and cheaper than conventional drug discovery, so far these companies have put only a few drugs into clinical trials, and none have made it through Phase III and FDA approval yet.

Early high-profile clinical failures last year, including Exscientia’s Phase I/II study of its cancer drug EXS-21546 and BenevolentAI’s Phase II study of its dermatitis drug BEN-2293, suggest the return on investment for AI in drug development may be further delayed.

The conversation around AI in the pharmaceutical industry seemingly portrays AI tools as a shortcut to clinic-ready compounds. This exaggerates the power of AI to master something as diverse and complex as human biology.

Why AI hasn’t lived up to the hype
Much of the excitement around AI in the pharmaceutical industry centers around the use of AI tools to identify new drugs by using sophisticated computer algorithms to crunch through publicly available datasets. Conventional wisdom suggests that the computer can’t be wrong. But if that were the case, why are we seeing challenges in moving from the dry lab to the clinic?

The answer likely lies in the data itself and in what companies do with their AI-derived findings. AI models are excellent at identifying correlations, but as we all know, correlations do not explain causation. The success of generative AI methods using data from public datasets is dependent upon the accuracy and completeness of the datasets used to train the AI models.

There is no evidence suggesting that we have yet fully digitized human biology. Even the most accurate and complete datasets can at best get researchers to identify proper correlations. Simply put, more data is needed. Companies developing drugs need to understand causation, which requires going back to the wet lab to validate AI-derived findings.

Real biological samples and longitudinal studies
In the present land grab for access to AI, there are few AI platforms focused on the use of real biological samples to feed their AI models, and even fewer using real biological samples in longitudinal studies to produce data. Few companies and groups are pioneering Bayesian AI versus more traditional machine learning models to derive insights from their samples.

OncoDxRx therefore strongly believes that with the use of real biological samples taken from the same patients at different times, AI can help researchers go beyond preset hypotheses and the traditional try-and-fail approach, and truly understand the causation of diseases and guide us to new discoveries. By validating pharmacological approaches in real world samples either prior to preclinical testing or as part of their efforts to understand the results of clinical trials, AI helps us not only make novel discoveries faster, but provide imperative insights to ensure clinical trial success.

What success with AI tools really looks like
Despite the early failures that have raised skepticism about the value of AI to pharmaceutical development, OncoDxRx remains bullish on the opportunity ahead. Rich, comprehensive, and free of human bias, AI tools can, with the support of wet lab validation and preclinical translational models, bring us closer to precision medicine and provide value across the value chain of pharmaceutical development.

AI-derived insights can help direct a path toward label expansion or drug repurposing once a therapeutic is approved, by leveraging understandings of the mechanism of action and responding population characteristics and identifying other patient populations with similar biological characteristics.

OncoDxRx Proposes: Biology-First Artificial Intelligence
OncoDxRx officials say that analyzing integrated circulating cell-free transcriptomics data with domain-specific AI-like models enables the company’s PGA (Patient-derived Gene expression-informed Anticancer drug efficacy) technology to better understand cancer biology for which it is predicting drug efficacy, and the biological changes that occur when its top-performance therapeutic candidates are administered.

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