Why We Need Artificial Intelligence to Predict the Probability of Success in Drug Development

Many of us know Moore’s Law, which, in short, says that the number of computer transistors (a measure of performance) doubles every two years while prices fall. So, over time, we get more computing power at a lower cost.

Few people know the opposite of Moore’s Law—aptly named Eroom’s Law—which states that drug discovery is becoming slower and exponentially more expensive over time despite technological improvements.

Significant effort and resources are being invested in reversing that trend. Artificial intelligence (AI) is one of the latest tools deployed to reduce costs and increase drug discovery and development productivity.

The reason why the hopes for AI are high lies in the ability of machine learning (ML) algorithms to detect patterns and learn similar to the way humans learn, just much faster and paired with the ability to ingest vast amounts of data in a short period of time. This is a critical feature as the amount of data available continues to multiply rapidly.

AI is already used at many stages in drug discovery and development, including target identification, virtual screening and optimization of compounds, prediction of drug response on cells and the efficacy of drug-target interaction, and after approval for the reporting of adverse effects, to name but a few. 

AI is now also being applied to additional critical decision points in drug development, such as assessing the probability that a drug in development will successfully complete clinical trials and receive approval from regulatory authorities, known as the probability of technical and regulatory success (PTRS).

What is PTRS? How is it used, and why is it so important?

The probability of technical and regulatory success (PTRS) is a quantitative assessment of the inherent risk a drug development program bears. This metric estimates the likelihood that a pharmaceutical or biotech product will successfully meet the technical (scientific and clinical) and regulatory (health authorities) requirements from development to market approval.  

PTRS assessments of clinical programs occur at various stages during drug development to support decision-making at critical stage gates. In particular, encoding a program’s inherent risk into a PTRS score helps biopharma companies decide whether to continue investing in a drug candidate or discontinue development. Consequently, this informs asset prioritization at the portfolio level, long-range planning and revenue forecasting, and resource allocation decisions.

Both decisions bear risks. Investing in a drug that ultimately fails wastes significant resources that could have been invested in another candidate. The earlier drug developers know a drug is unlikely to succeed, the better. “Failing early” saves potentially 10s or even 100s of millions of dollars in development costs. In addition, failing early also avoids subjecting volunteers and patients to unnecessary treatments.

The flip side of this risk is discontinuing a drug that would have been approved, which results in a missed opportunity to bring a novel therapy to market and provide patients with more treatment options.

The more accurately the probability of success can be assessed, the more informed drug developers’ go/no go and resource allocation decisions become. This improves the efficiency of the drug development process by enabling companies to prioritize programs that are more likely to succeed. Greater efficiency contributes to reversing Eroom’s Law and ultimately benefiting patients.

What are some of the common approaches to calculating PTRS in pharma?

PTRS assessments in pharma are an integral part of pharmaceutical decision-making. Companies have long been trying to evaluate the chances a drug will make it to the market. While the actual process varies from company to company, industry benchmarks are traditionally used as the starting point, adjusted by experts in their respective fields, and supplemented with statistical analysis.

This PTRS approach comes with challenges and limitations:

  • Industry benchmarks are inconsistent; some are based on just a few program characteristics, and others are based on many. Also, reliable, comprehensive industry-wide benchmarks are challenging to obtain and lack granularity.
  • Adjustments to these benchmarks are not often based on objective data and can be liable to overestimate the probability of success. Human bias comes into play, both cognitive and motivational. 
  • Statistical analysis, such as regression analysis of large data sets, is challenging and often static because new data cannot be incorporated into the analysis in a timely fashion, impacting the conclusion one can draw from the non-updated data sets.

Given these shortcomings, drug development has long needed a more objective, data-driven approach capable of analyzing vast amounts of data and seamlessly ingesting new data.

How can artificial intelligence (AI) improve PTRS assessments?

Large amounts of continuously updated data, complex interdependencies and subtle patterns are the conditions under which AI surpasses all other approaches.

AI, specifically the machine learning (ML) sub-discipline, has demonstrated that it can outperform drug developers and physicians when assessing the probability of success.

Machine Learning is defined as the use and development of computer systems that can learn and adapt without following explicit instructions by using algorithms and statistical models to analyze and draw inferences from patterns in data.

 ML has several significant advantages over traditional approaches:

  • The algorithms can ingest broader and much larger sets of input data
  • Sophisticated algorithms can identify inferences that analysts cannot spot by traditional means
  • Data from hundreds of disparate sources – both structured and unstructured datasets – can be integrated into structured ontologies for a single source of truth.

These rich data sources and superior analyses yield objective probability assessments throughout the drug development pipeline.

An additional feature, namely AI explainability, addresses the concern around the “Black Box” nature of these algorithms. AI explainability provides insights into the relative contributions of various features (e.g., drug biology, clinical trial design, regulatory designations, and outcomes) to the final PTRS prediction. It makes it easier for the user to trust AI-generated probabilities of success.

Benefits of AI-driven probability assessments in clinical development

PTRS predictions have long been used to help make consequential decisions in drug development. 

AI is a powerful tool to augment current methods and base drug approval and phase transition predictions on a highly objective, data-driven foundation that aids informed decision-making at critical decision points. 

The accurate assessment of the likelihood of clinical trials to succeed helps:

  • Improve the efficiency of drug development by giving companies an edge in identifying therapeutic programs that are more likely to succeed in clinical trials and should, therefore, be prioritized.
  • Companies fail drugs with little chance of success early on, potentially saving 10s of millions of dollars and years of effort. These resources can be redirected to more promising drug candidates.
  • Business development and licensing (BD&L) decisions allow acquirers to better establish the value of a drug candidate they are interested in buying and sellers to better understand the value of their assets. 

We need to leave the detrimental dynamics of Eroom’s Law behind and enter into a new era of efficient and more affordable drug development. Artificial intelligence is a powerful tool in this process. Applying it to support decision-making at critical points will benefit pharmaceutical and biotech companies and, most importantly, patients.