AI and Machine Learning in Pharmaceutical Drug Development – Asrar Qureshi’s Blog Post #1102

AI and Machine Learning in Pharmaceutical Drug Development – Asrar Qureshi’s Blog Post #1102

Dear Colleagues! This is Asrar Qureshi’s Blog Post #1102 for Pharma Veterans. Pharma Veterans Blogs are published by Asrar Qureshi on its dedicated site https://pharmaveterans.com. Please email to pharmaveterans2017@gmail.com  for publishing your contributions here.

Credit: cottonbro studio

Credit: Rui Dias

Credit: YI REN

This blogpost is inspired by interview of GSK’s Global Head of AI and Machine Learning, Kim Branson, by McKinsey panel. Link at the end

Preamble

The pharmaceutical industry is undergoing a profound transformation. With rising R&D costs, extended development timelines, and increasing regulatory scrutiny, innovation is no longer optional—it is essential. Among the most disruptive enablers of this transformation are Artificial Intelligence (AI) and Machine Learning (ML). These technologies are rapidly changing the way drugs are discovered, developed, and delivered.

From early-stage research to clinical trials and post-market surveillance, AI and ML are streamlining processes, accelerating timelines, and improving decision-making. Let’s explore how these tools are shaping the future of pharmaceutical product development.

Pakistan situation is quite different because the R&D here is limited to generic product development in a rather loose framework. I shall talk about it separately in this post.

Accelerating Drug Discovery

Drug discovery has traditionally been a labor-intensive, high-cost endeavor with high failure rates. AI and ML now offer the ability to sift through massive volumes of biological, chemical, and clinical data to identify new drug candidates faster and more efficiently.

 Key Innovations

- Target identification: ML algorithms can analyze genomic, proteomic, and phenotypic data to identify novel therapeutic targets.

- Molecular screening: AI models simulate interactions between thousands of compounds and targets, predicting efficacy and toxicity without wet-lab testing.

- De novo drug design: Generative AI can design entirely new molecules based on desired biological activity and properties.

These capabilities significantly reduce the early-stage trial-and-error process, improving the probability of success.

Enhancing Preclinical Development

Preclinical development involves evaluating safety, pharmacokinetics, and pharmacodynamics in vitro and in animal models. AI can optimize this phase by predicting toxicological outcomes and modeling human biology more accurately than traditional methods.

 Use Cases

- Predictive models for ADMET (absorption, distribution, metabolism, excretion, and toxicity)

- Digital twins to simulate biological systems

- Image analysis for histopathology and biomarker discovery

This minimizes animal testing, speeds up validation, and reduces preclinical attrition rates. As reported by me in the Newsletter earlier, the USFDA has already decided to phase out animal testing for drug development.

Revolutionizing Clinical Trials

Clinical trials are the costliest and lengthiest phase in drug development. AI and ML bring much-needed efficiency and intelligence to trial design, execution, and monitoring.

 Key Contributions

- Patient recruitment and retention: ML models can identify ideal patient populations by analyzing real-world data and EHRs.

- Adaptive trial designs: AI can support dynamic adjustments to trial protocols based on real-time data.

- Remote monitoring: AI-powered wearables and digital tools collect and interpret patient data, enabling decentralized trials.

The result is faster enrollment, improved compliance, and more reliable outcomes.

Personalized Medicine and Precision Therapeutics

The integration of AI with genetic and patient-specific data paves the way for truly personalized treatment strategies.

 Applications

- Predictive analytics to determine treatment responses

- ML algorithms to customize dosages and regimens

- Biomarker-based patient stratification

This leads to better therapeutic efficacy, reduced side effects, and optimized healthcare outcomes.

AI in Formulation and Manufacturing

Beyond discovery and trials, AI also supports innovation in pharmaceutical formulation and manufacturing.

Key Innovations

- Predicting excipient compatibility and formulation stability

- Process optimization in continuous manufacturing

- Real-time quality control using AI-integrated sensors and analytics (e.g., PAT frameworks)

Smart manufacturing ensures higher product quality, cost savings, and compliance with regulatory standards.

Regulatory Compliance and Pharmacovigilance

AI enhances safety surveillance and compliance by continuously monitoring data and identifying adverse events early.

 Examples:

- NLP tools to analyze spontaneous reporting systems and patient forums

- Automated signal detection for post-market surveillance

- AI-powered documentation systems to streamline regulatory submissions

This not only improves patient safety but also helps pharmaceutical companies respond faster to regulatory requirements.

Challenges and Ethical Considerations

While the potential is enormous, integrating AI into pharmaceutical development comes with challenges:

- Data privacy and security concerns

- Bias in training datasets

- Interpretability of AI models

- Regulatory uncertainty around AI-driven decisions

Addressing these issues will require cross-disciplinary collaboration, robust governance frameworks, and updated regulatory guidance.

Pakistan Scenario – Where AI/ML Fit?

No pharmaceutical company has done drug development yet in Pakistan, and it does not appear to be likely any time soon. There are many reasons besides limited resources which include but are not limited to technical expertise, lack of industry-academia collaboration, and absence of accredited labs for carrying out toxicology, pharmacokinetics, and bioequivalence studies. 

Having said that, AI and ML can help in two major areas.

Formulation Development: is the key part of product development. AI can predict which excipients will be more compatible with the drug molecule to keep it stable, make it bioavailable, and control time to dissolution, suitable packaging etc.

Post Marketing Surveillance: Generic drug manufacturers are free to develop formulations without regard to the innovator products’ specs, such as tablet size, weight, even color. The excipients used by our manufacturers are also not the same as used by the innovator. Post Marketing Surveillance will shed important light on the own generic brand vis-à-vis its behavior in various patient populations. It will help the marketing to promote the products in a more targeted fashion and it will help improve formulation development. 

Since only a handful companies are likely to use these tools, it will give them huge competitive advantage to gain even greater market share.

Sum Up

AI and Machine Learning are not just tools; they represent a paradigm shift in how pharmaceutical products are conceived, tested, and brought to market. By automating routine processes, uncovering hidden patterns, and enabling data-driven decisions, these technologies can dramatically reduce costs, improve outcomes, and deliver value to patients faster.

Pharma companies that embrace AI early and responsibly will not only drive innovation but also lead the future of healthcare. The fusion of human intelligence with machine learning will unlock possibilities once thought impossible—and reshape the industry for the better.

Concluded.

Disclaimers: Pictures in these blogs are taken from free resources at Pexels, Pixabay, Unsplash, and Google. Credit is given where available. If a copyright claim is lodged, we shall remove the picture with appropriate regrets.

For most blogs, I research from several sources which are open to public. Their links are mentioned under references. There is no intent to infringe upon anyone’s copyrights. If, any claim is lodged, it will be acknowledged and recognized duly.

Reference:

https://www.mckinsey.com/industries/life-sciences/our-insights/gsks-kim-branson-on-driving-innovation-with-ai-and-machine-learning?stcr=E49DF8AC69DB49C483074FF6A9F338E8&cid=other-eml-alt-mip-mck&hlkid=4517fb302aeb4777aeb2cfee3792024e&hctky=15999472&hdpid=60ad23a4-1323-4831-99aa-f90ff8aaab05