FAQ: Key AI Applications in Drug Development

1. How are computational models and AI/ML being leveraged in early-stage formulation development?

Up to 90% of drug candidates currently in development are poorly soluble, posing a significant risk to commercial success. To mitigate this, artificial intelligence (AI) and machine learning (ML) are utilized in silico to accelerate early-phase development by predicting optimal solubilization technologies, such as amorphous solid dispersion formulations.

Advanced models, including molecular dynamics simulations and quantum mechanical calculations, analyze molecular descriptors (like potential energy surface charge models and hydrogen bond donors/acceptors) to characterize drug–excipient interactions and predict maximum feasible drug loading. This systematic in-silico framework guides the selection of excipients, minimizes API consumption, and reduces reliance on empirical trial-and-error, thereby saving time and resources.

2. What is the mechanism by which AI-powered digital twins accelerate preclinical evaluation?

Pharmaceutical R&D faces hurdles due to the high cost and low translatability of conventional preclinical models. Digital twins—computer simulations of physical systems—are trained using large-scale, multi-modal data gathered from ex-vivo lung perfusion systems, which provide "clean" data on isolated human organs. These models, incorporating physiology, biochemistry, and transcriptomic data, have achieved greater than 90% accuracy in forecasting lung function.

This innovation creates a personalized digital control arm for every treated organ by generating the counterfactual outcome (the untreated effects). This capability allows researchers to perform a paired statistical analysis, enabling direct comparison between the observed treatment and the digital twin-generated outcome within the same organ. This method has revealed therapeutic effects missed by traditional two-arm studies and is designed to accelerate drug discovery by reducing the required study size.

3. How is AI transforming bioanalytical and manufacturing workflows?

AI, ML, and large language models (LLMs) are enhancing bioanalysis by improving quality, efficiency, compliance, and reducing human error. Early validated applications include automating report writing and quality control checks that integrate data from electronic lab notebooks and laboratory information management systems to identify failure trends early. LLMs automate the creation of study protocols, validation reports, and can draft bioanalytical sections of electronic common technical documents. AI also optimizes method development for assays, including ligand binding and liquid chromatography coupled with mass spectrometry, by predicting optimal conditions and performing intelligent peak detection. Health authorities are also looking at AI to analyze data submitted in filings and during on-site inspections.

In manufacturing, AI is expected to revolutionize process outcomes—including yield, right-first-time rates, and tech transfer speed—by accelerating process characterization through pattern identification across large datasets. This capability is especially critical for high-variability modalities like cell and gene therapies.

4. What are the primary barriers to wider AI adoption in the pharmaceutical industry?

The most substantial barrier to AI implementation is the prerequisite for digitalization and the lack of high-quality data. Many organizations do not realize how disparate or dispersed their legacy systems are until implementation begins, requiring significant preliminary effort for data consolidation and cleansing (data hygiene). Organizational constraints, such as fragmented AI strategies and internal silos, further hamper wider adoption.

Regulatory guidance is rapidly emerging and centers on a risk assessment approach that evaluates how the AI model’s behavior impacts the final drug product’s quality, safety, and efficiency for the patient. For regulated bioanalysis, controls must be in place to prevent the risk of hallucination (creation of data not provided), requiring audit trails to ensure compliance.