The pharmaceutical landscape is quickly evolving as new technologies propel innovation across the clinical supply chain. Machine learning-powered (ML) predictive analytics are enabling manufacturers to foresee potential transportation issues before they become supply chain disruptions, Bluetooth technology allows for patient monitoring and adherence through remote visibility and reporting for more diverse clinical trials, and artificial intelligence (AI) is being adopted for quality assurance and quality control.
Advanced technologies like AI and ML are also transforming early drug development by providing solutions to common challenges. One of the most significant challenges in early drug development is progressing poorly soluble molecules from discovery to clinical trials, as poor solubility generally corresponds with lower bioavailability, which can have a detrimental effect on the efficacy of the drug product. Historically, resolving poor solubility was done through trial and error, but AI/ML tools have helped tackle this challenge by accurately predicting the most effective combinations of solubility enhancement technologies and formulations for drug development.
Addressing Poor Solubility in Early Drug Development
The solubility and bioavailability of a molecule depend on various physico-chemical factors, as well as the properties of the excipients and formulations. Some estimates suggest that the bioavailability of more than 70-90 percent of molecules is challenged by solubility issues.1 Even in the hands of experienced formulation scientists, identifying the most effective combinations is a complex and intricate process, requiring detailed experimentation and analysis.
Employing traditional formulation development methods requires significant time and resources for systematic experimentation, data collection, and analysis. This impacts both drug development timelines and budgets, but today, scientists have a variety of new AI/ ML predictive modeling tools at their disposal and can efficiently find the best pathway to improve the drug’s bioavailability.
The Rise of Next-Generation Technologies
Advancements in AI/ML technologies, combined with an increase in regulatory submissions involving these methods, have led the US Food and Drug Administration (FDA) to establish multiple specialized groups to provide guidance and additional oversight specific to this area. These groups are tasked with accumulating knowledge, providing support, and assessing and driving innovation in AI/ML applications within the pharmaceutical sector.2 Recently, the pharmaceutical industry has seen published research that highlights the expanding role of and use cases for AI in computational chemistry.3 The anticipated growth of the global AI market in drug discovery, which is projected to reach a value of $4.9 billion by 2028,4 further underscores this investment.
Predictive modeling and similar methods are also revolutionizing formulation development. These techniques address long-standing industry challenges, particularly those related to improvements in solubility and bioavailability. Advances in techniques like quantum mechanics and molecular dynamics (QM/MD) are critical components of this transformation. They offer precise modeling capabilities and provide deep insights into molecular-level interactions, such as ligand binding, structure, and analytical characterization. These approaches are proving more reliable and accurate predictions when compared to traditional methods, which involve experimental screening of multiple technology options and numerous formulations during the initial set-up phase in an experiment.
With growing interest in its applications from lead generation, formulation design, and prediction of clinical trial results to advanced pharmaceutical manufacturing, AI-driven computational methods are empowering innovation across even more aspects of drug development, further aiding efficiency.
Partnering with Contract Manufacturers for AI/ML Predictive Modeling
For formulation scientists contending with solubility challenges in their compounds, it can be beneficial to collaborate with a Contract Development and Manufacturing Organization (CDMO) specializing in computational drug development. CDMO partners committed to staying at the forefront of pharmaceutical innovation are adopting new technologies focused on speed, scalability, and innovation as a part of their end-to-end offerings. This supports biopharma and pharmaceutical companies across the drug development journey, from molecule to medicine, ensuring the faster delivery of high-quality therapies to patients.
An example of this investment is the availability of AI/ML-based solutions that can assist in early formulation development through in silico screening. Developers can input the molecular structure and known physicochemical properties of their compound into a platform that generates predictive models across multiple therapeutic areas and druggable spaces for small molecule oral delivery modalities.
There are solutions with proprietary algorithms, including QM/ MD, quantitative structure-activity relationship (QSAR) models, and aspects of ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis that can create customized predictions. Utilizing specific data of the compound, these algorithms can identify promising solubility enhancement techniques and appropriate formulation designs for enabling technologies (e.g., amorphous dispersions). With the generated analysis, experts can review the insights to make holistic recommendations that align with clinical objectives.
This in silico approach to formulation development aims to reduce the time and resources typically expended on trial-and-error methods. It addresses the risk associated with potentially having to revise solubility enhancement approaches post-proof of concept, which could otherwise lead to significant delays and additional costs in the development process.
The growing preference for in silico modeling over traditional trial-and-error methods is primarily driven by the potential for significant time and resource savings. Many pharmaceutical companies initially adopt conventional approaches for resolving solubility challenges, but as time and budget costs accumulate, they look for guidance in transitioning to an approach that integrates AI/ML technology. Additional critical benefits include the reduction of risk and minimization of barriers in each phase of development. Predictive modeling can even help developers reduce the amount of active pharmaceutical ingredient (API) required and support sustainability by reducing energy use and material waste.
The Future of AI/ML for a Patient-Centric Approach to Drug Development
To date, AI/ML has shown continuous success in generating high-quality data for scientists during different stages of the drug discovery and development process. It has allowed for large data sets that have been curated, standardized, and represent the druggable formulation space, ensuring better performance of the predictive technologies and algorithms. Advances in computing will enable the processing of larger, more complex datasets more efficiently. In turn, this will facilitate the development of more sophisticated computational models which will enhance our understanding of the behavior of complex molecular systems, improve the speed of new API discovery, and increase the predictive accuracy of formulations. The incorporation of real-world data – including clinical trial results, patient outcomes, and safety surveillance data – promises to further drive the precision and accuracy of predictions.
These innovations offer great promise for addressing the unmet needs of patients for whom effective treatments are not yet available. As the trend toward patient-centric solutions continues to drive innovation in the pharmaceutical industry, new technologies will continue to be incorporated across the clinical supply chain to bring life-saving therapies to those who need them most. The sizable reduction in development costs with AI/ML may even represent lower drug costs for consumers, allowing or improving access for many.5
As with all new research spaces where there is a lot of excitement and many innovators moving concurrently, widespread collaboration on AI/ML could have resounding benefits among stakeholders. To further support the advancement and adoption of these tools, creating government initiatives to develop accuracy standards could help protect the reputation of AI/ML use across multiple roles and lead to increased confidence in these new technologies as they aid in drug development.
Regulators will also need to establish ways to consistently evaluate submissions involving AI/ML. The FDA has announced its plans to “develop and adopt a flexible risk-based regulatory framework that promotes innovation [with respect to the use of AI/ML] and protects patient safety”.5 The administration has acknowledged the opportunities and challenges associated with AI/ML and is currently seeking feedback from stakeholders on several issues related to its use in the development and manufacturing of drugs.
The Integration of AI/ML Marks a Paradigm Shift
The integration of AI/ML technologies in drug development, particularly in addressing solubility and bioavailability challenges, marks a significant paradigm shift in the pharmaceutical industry.
These advanced computational methods are transforming the traditionally slow, costly, and resource-intensive processes into more streamlined, efficient, accurate, and cost-effective strategies. AI/ML tools are not only optimizing the selection of solubility enhancement technologies and excipients, but they are also paving the way for a more streamlined and sustainable development process.
This technological revolution in drug formulation is poised to accelerate the journey of new drugs from the laboratory to the patient, potentially improving outcomes and accessibility. As the industry continues to evolve, embracing these technological advancements will be key to overcoming long-standing challenges and unlocking new possibilities in the quest for more effective and accessible treatments. The future of drug development, powered by AI/ML, holds immense promise for both the pharmaceutical industry and patients worldwide.
References
- Loftsson T, Brewster ME. Pharmaceutical applications of cyclodextrins: basic science and product development. J Pharm Pharmacol. 2010 Nov;62(11):1607-21. doi: 10.1111/j.2042- 7158.2010.01030.x. PMID: 21039545.
- US Food and Drug Administration. Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. 2023. Available at: https://www.fda. gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development
- Dral, Pavlo O. “AI in Computational Chemistry through the Lens of a Decade-Long Journey.” Chemical Communications 60 (2024): 3240-3258. https://doi.org/10.1039/D4CC00010B.
- Artificial intelligence/AI in drug discovery market: Global forecast to 2028. Markets and Markets website. 2024. Available at: https://www. marketsandmarkets.com/Market-Reports/ai-in[1]drug-discovery-market-151193446.html.
- AI Drug Discoveries to Cut Costs and Save Lives: Medicine’s Next Big Thing? [Blog post]. University of Central Florida. Available at: https://mse.ucf.edu/ ai-drug-discoveries-to-cut-costs-and-save-lives-medicines-next-big-thing/
Author Details
Sanjay Konagurthu, PhD - Senior Director, Science and Innovation, Pharma Services, Thermo Fisher Scientific
Publication Details
This article appeared in American Pharmaceutical Review:Vol. 27, No. 3April 2024Pages: 28-31
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