After nearly a century of regulatory demand for the reliance on inaccurate (and increasingly controversial) animal testing methods, shifts in both science and consumer demands have finally pushed through updates to antiquated regulations and opened the door for a major evolution in how pharmaceutical companies manage drug safety testing.
Drug researchers and pharma companies are now starting to embrace non-animal testing models thanks in part to an artificial intelligence (AI) innovation wave set to transform the drug development process and personalized medicine efforts.
Key forces
While animal testing has been mandated for every drug since the 1930s, the drug development framework is changing — a long overdue and much needed change. Put simply, animal models cannot properly predict how a drug will work in the human body. There are too many critical differences in organ systems and drug-metabolizing enzymes across human subgroups let alone animal species.
An abysmal nine out of ten drug candidates successfully pass animal testing and then later fail clinical trials — making potentially life-saving drugs more expensive and more time consuming for pharmaceutical companies to produce, as well as harder for patients to access.
A push to modernize drug testing regulations to incorporate non-animal methodologies is happening around the globe, with funding increases for alternative research methods following in lockstep. Armed with the ideal trifecta of regulatory changes, consumer advocacy and advances in AI, pharmaceutical leaders are finally able to start evolving beyond the boundaries of traditional time- and cost-intensive pre-clinical processes built on ineffective animal testing practices.
The power, and limitations, of AI
AI-powered approaches that reduce the reliance on animal testing are no longer science fiction. Major progress has been made spanning innovations in organ-on-chip technology, human cell-based assays, 3D tissue models, and advanced computer simulations that can reduce the dependence on antiquated, inaccurate animal-based testing. From applying computational platforms to genomic analyses and the refinement of early-stage target validation, to novel machine learning approaches that can predict precise protein structures and the detailed designs of small molecules, AI has become a primary tool in a more efficient drug discovery and development process.
AI is intertwined in the advancement of every aspect of the future of effective drug safety and efficacy screenings. Groundbreaking patient-on-a-chip technology, which integrates advanced AI with interconnected organ-on-chip systems and nano-sensing capabilities, simulates the potential drug response in actual patients to help researchers better understand how a drug will impact the entire human body.
Meanwhile, induced pluripotent stem cell (iPSC) technologies continue to strengthen in-vitro testing capabilities to deliver faster, more precise information. Pharmaceutical companies can now evaluate novel compounds across a diverse patient population before ever involving a human patient, allowing R&D teams to optimize study design, eliminate non-viable candidates earlier and, ultimately, get safer drugs to patients faster.
Not only will AI support pharma companies ready to reduce animal testing, but it will also support the creation of a better healthcare ecosystem to help people live longer, healthier lives. Tapping into the power of AI to automate and improve drug safety predictions will speed time-to-market and minimize R&D costs, while also preventing devastatingly disappointing and expensive late-stage failures and improving patient outcomes.
In addition, AI can help reduce primary resource burdens that limit many promising drug trials — from recruiting and retaining trial participants to automating key elements of trial data with sensors and wearables and keeping critical timelines on track. Beyond initial novel drug development and drug safety testing, AI also provides a path to make personalized medicine a reality by creating new ways to effectively determine optimal dosing for a specific patient, unlock new drug combinations and avoid harmful drug interactions.
With the justified excitement around the transformative power of AI, a word of caution is due. AI is not a “magic bullet”. It is only as good as the data it is trained on, and it is dependent on continuous, accurate, high-throughput, biological validation technologies that can continuously assess its accuracy. Bio-AI approaches that integrate AI with advanced biology technologies — like the ones mentioned above — therefore hold great promise.
A new era in sight
Conventional drug development practices are currently undergoing an AI-driven evolution — one which will optimize drug discovery and development processes at each point in the novel drug lifecycle. AI-powered innovations, especially when integrated with advanced biology platforms, are taking root and, overtime, will become a fixture in the pharma industry as a more reliable way to determine the safety and efficacy of new drugs.
As we embark on a new era of more effective AI-powered drug safety efforts, both pharma companies and patients alike will benefit. From enhancing target discovery and molecule design, to drug-target fit and mode-of-action analysis, AI is improving every aspect of drug discovery and development while also helping the industry put patient efficacy, wellness and personalized needs properly in focus.
