Revolutionising Pharma: The Impact of AI on Drug Development

November 22, 2023

Over recent years, the application of artificial intelligence (AI) in the pharmaceutical and biomedical fields has transitioned from a futuristic concept to a practical, scientific reality. Pharma and biotech companies are increasingly turning towards more effective, automated methods that integrate data-driven decision-making and utilise predictive analytics. This represents a significant shift towards embracing AI and machine learning for advanced data analysis.

The AI employed in the pharma industry and beyond is a specialised form of machine intelligence, distinct from the AI depicted in science fiction films. It is designed to perform specific tasks using automated algorithms, rather than taking over the world.

The aim of this AI technology is to uncover patterns and insights from large data sets, achieving what would be impossible for humans. Its application in data mining and analytics is revolutionising sectors like pharma and biotech, with uses spanning drug discovery, production process automation, and clinical applications, including medical imaging and surgical robotics.

Types of Artificial Intelligence in Healthcare

Currently, most AI solutions in healthcare rely on data science algorithms created by humans. This AI form uses multivariate data analytics, drawing on historical evidence to combine treatment outcomes with individual patient data for personalised treatment and drug combination recommendations.

Machine learning represents another AI aspect, based on neural networks that imitate human brain functioning. These data-driven algorithms allow software to accurately predict outcomes without specific programming.

Deep learning, a more advanced form of AI, also uses neural networks but incorporates multiple layers of calculations and signals. This technology holds significant potential for diagnostics, accurately analysing images and pathology data alongside historical treatment results.

AI Applications in Pharma

AI’s role in biopharma is expanding, with its market value expected to reach $10B by 2024. This includes applications in medical imaging, diagnostics, personal AI assistants, drug discovery, and genomics.

In the biopharmaceutical industry, AI applications include:

Manufacturing Process Improvement

AI enhances development and production, offering opportunities for process optimization. It aids in quality control, design time reduction, material waste minimization, production efficiency, predictive maintenance, and more.

AI’s role extends to enhancing production efficiency, such as using CNC (computer numerical control) to replace manual data input or management. AI algorithms not only ensure precision in tasks but also analyse processes for potential streamlining, leading to reduced material waste, accelerated production, and consistent adherence to Critical Quality Attributes (CQAs).

Advancing Drug Discovery and Design

Artificial Intelligence (AI) is revolutionising the field of drug discovery and design, from conceptualising new molecular structures to pinpointing groundbreaking biological targets. AI’s role spans various facets of drug development, including target identification and validation, target-based and phenotypic discoveries, multi-target drug exploration, drug repurposing, and biomarker detection. A key advantage for pharmaceutical companies is AI’s ability to expedite drug trials, significantly shortening the time required for drug approval and market entry, potentially leading to cost reductions and more affordable drugs for patients, alongside a broader range of treatment options.

For instance, pharmaceutical researchers are now able to discover and validate new cancer drug targets using comprehensive data sources like electronic medical records, next-generation sequencing, and diverse ‘omic data to construct detailed patient models.

Processing Biomedical and Clinical Data

One of the most prominent applications of AI to date is in algorithms capable of processing and interpreting vast amounts of textual data. This technological advancement is a game-changer for life sciences researchers, offering a more efficient method to sift through the ever-increasing volume of research publications for hypothesis validation.

Moreover, clinical studies often rely on patients’ handwritten diaries, detailing medication schedules and any adverse reactions. AI has the capacity to collate and interpret a wide array of data, ranging from handwritten notes and test results to environmental factors and imaging scans. The utilisation of AI in this manner can lead to accelerated research, streamlined data cross-referencing, and the transformation of diverse data types into analyzable formats.

A study by Cognizant revealed that approximately 80% of clinical trials do not meet enrollment deadlines, with a third of Phase III clinical study terminations attributed to enrollment challenges.

Rare Diseases and Personalised Medicine

AI is increasingly employed in various capacities, such as analysing body scans and patient biology, to detect diseases like cancer and predict potential health issues based on genetics. An example of this is IBM Watson for Oncology, which tailors treatment plans based on each patient’s medical history.

Additionally, AI is instrumental in creating personalised drug treatments, considering individual test results, responses to previous medications, and historical data on drug reactions.

Streamlining Clinical Trial Candidate Identification

Beyond deciphering clinical trial data, AI plays a crucial role in identifying suitable clinical trial participants. Utilising advanced predictive analytics, AI can evaluate genetic data to determine the appropriate patient group for a trial and ascertain the optimal sample size. Certain AI technologies are capable of interpreting free-form patient responses in clinical trial applications, as well as unstructured data like doctors’ notes and intake documents, enhancing the trial process.

Predicting Treatment Outcomes

One of the key cost-effective benefits of artificial intelligence in healthcare is its capability to tailor drug treatments to individual patients. This reduces the reliance on the traditional trial-and-error method. Machine learning algorithms can predict how a patient might respond to various drug treatments by analysing a range of factors, including how the body absorbs and distributes compounds and individual metabolic processes.

Predictive Biomarkers

Developing biomarkers is crucial, not just for medical diagnostics but also in the realm of drug discovery and development. Predictive biomarkers, for instance, are instrumental in identifying potential candidates for molecular targeted therapies before human testing. AI leverages large datasets to “train” biomarker models, enhancing the precision and effectiveness of this process.

Drug Repurposing

For budget-conscious pharmaceutical companies, AI’s potential in drug repurposing is particularly promising. Reassigning existing drugs or late-stage candidates to new therapeutic areas can be a lower-risk and more cost-effective strategy. AI facilitates this process by reducing the likelihood of unexpected human trial toxicity or side effects, as well as minimising research and development expenditures.

Drug Adherence in Clinical Trials

A major challenge in pharmaceutical studies is ensuring participants adhere to the drug study protocol. AI can play a significant role in monitoring compliance remotely and evaluating test results. By distinguishing between compliant and non-compliant participants, AI helps maintain the integrity of clinical trial results.

The Gradual Adoption of AI in Pharma Development

While the benefits of implementing AI in pharmaceutical and biotechnological development are clear, the transition towards these technologies can be gradual. Traditional drug development methods need time to adapt to what might be seen as a technological “disruption.” Moreover, training AI for drug discovery and development can be a lengthy process, as feedback on new drug candidates often takes months or years, unlike instant feedback in applications like social media photo tagging.

Yet, the integration of AI in the pharmaceutical industry is inevitable and will be a game-changer. Companies that embrace and integrate these new technologies will gain a strategic edge. A practical starting point is the adoption of existing data analytic technologies that utilise multivariate and predictive analytics.

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Published on 22-11-2023