AI for Clinical Trials
India is looking to replace animals in clinical trials. With all the added benefits AI can offer, there is no reason why the inhuman practice of animal testing must continue
A recent amendment to the New Drugs and Clinical Trial Rules (2023)passed by the Government of India, aims to do away with the use of animals in research – especially in drug testing. The amendment permits researchers to utilise non-animal and human-relevant methods for testing the safety and effectiveness of new drugs. Such methods include technologies like 3D organoids, 3D bioprinter, organs-on-chip, and advanced computational methods like AI, to test the safety and efficacy of new drugs. The new 2023 rules amend the previous 2019 version.
A necessary evil?
Around 20 million animal subjects are used annually in biomedical research worldwide. It has always been considered a necessary evil. Over the years, a lot of debates have ensued regarding the ethics and the limitations of using animals in drug testing. The major arguments against the practice are:
- Such trials have limited success in human responsebecause human biological processes are not exact replications of animal systems.This is the reason why despite passing animal testing, many drugs fail during human clinical trials.
- Thehigh failure rate in translating to human efficacy makes the procedure performed on the animal seem needless.
- This leads to the ethical aspect of the practice. Causing suffering and harm to sentient beings who are unable to defend or express themselves goes totally against morality.
- Afailure in human trial post animal testing also means wasted resources and harm to human participants.
- Animals used in testing do not adequately represent the diversity of human populations in terms of age, sex, genetic variations, and underlying health conditions. Hence, such trials can never be exhaustive.
- The regulatory requirements and ethical-legal considerationsinvolved in animal testingtranslate to delays, increased costs, and obstacle to approvals.
In search for an alternative
The world is gradually waking up to the needless cruelty involved in animal testing. In a major shift, a legislation signed by US President Joe Biden in December 2022 proclaims that new medicines need not be mandatorily tested on animals to receive US Food and Drug Administration (FDA) approval.
South Korea, too, introduced a Bill in 2022 on the ‘Vitalization of Development, Dissemination, and Use of Alternatives to Animal Testing Methods’.
In 2021, the European Union passed a resolution on an action plan to facilitate the transition towards technologies that don’t use animals in research, regulatory testing, and education.
More recently, in June 2023, Canada amended its Environmental Protection Act to replace, reduce or refine the use of vertebrate animals in toxicity testing.
Dr Gaby Neumann of the Doctors Against Animal Experiments association estimates thatfewer than one in ten drugs that are successful in animal testing actually receive market approval – a shockingly low rate. This is mainly due to the lack of transferability of animal test results to humans.According to Neumann, Artificial intelligence could play a much more efficient role here.
In a recent lecture delivered in the regional training programme for Committee for Control and Supervision of Experiments on Animals (CCSEA), P. Rama Rao, Dean, GITAM Institute of Pharmacy, confirmed thatAnimal testing can be replaced with Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning.Toxicity prediction models using AI can aid in achieving scientific accord and meeting regulatory applications. With the enormous progress achievedin AI technology, and its application in fields such as medicine and chemistry, such alternatives are fully capable of replacing animal models, he said.
The scope of AI in clinical trials
Drug development is a complex and costly process that typically takes years to complete. Before pharmaceutical companies can go to market with a breakthrough drug, they need to ensure safety and efficacy through rigorous testing and evaluation in clinical trials. This process is slow, expensive and unpredictable. AI and machine learning are increasingly being leveraged to accelerate the process and optimise clinical trial outcomeswithout ignoring safety concerns.
The scope for using AI in drug development is vast indeed:
- Clinical trials involves phases that evaluate drug safety, side effects and effectiveness. In each of these phases, data is obtained from participants and monitored for adverse reactions or changes to health indicators. In pharma R&D, this can include data from electronic health records, administrative records and health surveys. This is an area where AI-based technologies are increasingly being used for improving the accuracy and efficiency of testing, accelerate drug development and optimise outcomes. This allows for more comprehensive and accurate data collection and management.
- Advanced algorithmscan quickly analyse vast databases of chemical compounds and identify those most likely to bind to a target. Algorithms can also predict the toxicity of a compound and its potential side effects, allowing researchers to focus their efforts on the most promising candidates.
- AI also enables automating patient recruitment and data collection, and can also provide crucial insight into participant behaviourby analysing the data. Algorithms can sift through large amounts of patient data to quickly identify potential participants based on specific inclusion and exclusion criteria.AI-powered chatbots and virtual assistants can be put to good use for this.
- AI algorithms are also good at detecting patternsin real time within the vast amounts of data generated by clinical trials. Such patterns help reveal trends that is impossible to detect using protracted traditional methods and allow researchers to make informed decisions within a short time.They can also use the tool to identify potential challenges or risks, avoid duplicate work and consult with experts to optimise the trial and improve efficacy.
- AI-powered chatbots and virtual assistants can provide patients with information about the trial, answer their questions and collect preliminary data. This reduces time and resources spent on manual patient screening and interviewing.
- AI-based predictive modelling can help researchers identify patient populations best suited for specific treatments and adjust trial design accordingly. Predictive modelling also identifies potential safety concerns early in the drug development process.
- Traditional methods of adverse event detection rely on manual reporting, which can be time-consuming and error-prone.AI streamlines this process by identifying potential adverse events more quickly and accurately by using machine learning algorithms.
- In clinical trials, natural language processing (NLP) can be harnessed to automate extraction and analysis of unstructured data from various sources, such as electronic medical records and patient-reported outcomes.
Overall, harnessing the power of AI to clinical trials results in faster time to market, reduced costs, more accurate data analysis, personalised medicine, improved patient outcomes, and real-time access to expertise. With all the added benefits AI can offer, there is no reason why the inhuman practice of animal trials must continue.
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