The pharmaceutical industry is increasingly focused on the concept of acceleration. Adaptive clinical trial designs and an expanding set of expedited regulatory pathways have been introduced to shorten development and review timelines for certain classes of therapies. These mechanisms create the possibility for individual programs to pass more swiftly through parts of the pipeline than was previously feasible. While this has not resulted in a general contraction of development timelines across the industry, it has altered the structure of the process itself.1,2
At the same time, the nature of what is being developed is undergoing fundamental changes. Over the past two decades, the industry has shifted away from a primary reliance on traditional small molecules toward a more diverse set of therapeutic approaches, including biologics, cell and gene therapies, RNA-based medicines, and microbiome-directed interventions. Emerging strategies such as targeted protein degradation and CRISPR-based gene editing further expand this landscape, while computational methods are increasingly integral to how therapies are conceived and optimized.3
These shifts create new opportunities, but they also reshape the development process. As platforms become more complex and programs more tightly linked to specific biological mechanisms and patient subgroups, technical development can no longer be treated as a downstream activity. Analytical, process, and formulation functions increasingly determine whether a candidate can be translated into a stable, scalable, and regulatorily acceptable medicine at all – making technical development not just a support for innovation, but one of its central constraints.
The key pillars of pharma technical development
Pharmaceutical technical development can be understood in terms of three core domains: analytical development, process development, and formulation development.
| Domain | Focus | Key activities |
|---|---|---|
| Analytical | Measure & characterize | Identity, purity, potency, stability, involving chromatography, MS, spectroscopy, dissolution, bioassays |
| Process | Manufacture | Route selection, optimization, scale-up, involving cell line and upstream/downstream processing |
| Formulation | Delivery | Dosage form, excipients, solubility, stability, delivery systems |
Historically, these activities have relied heavily on manual, labor-intensive, and largely empirical workflows. Optimization has often proceeded through sequential trial-and-error experiments, with quality assessed primarily through end-product testing rather than real-time control. While this approach has enabled decades of successful development, it is slow, resource-intensive, and vulnerable to late-stage failure – and it becomes increasingly limiting as timelines tighten and therapeutic platforms grow more complex.4
Automation, digitalization, and AI as enablers
Automation, digitalization, and AI/ML provide practical ways to reduce some of the structural bottlenecks in pharmaceutical development. In discovery and early development, AI-based virtual screening can evaluate large chemical spaces and predict interactions between candidate molecules and biological targets. For example, platforms such as AtomNet use machine-learning models to prioritize compounds more efficiently than traditional screening alone.5
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Generative approaches extend this further by proposing novel molecular structures and iteratively optimizing them for properties such as potency and predicted pharmacokinetics. A case study from Insilico Medicine illustrates how these methods can accelerate early discovery: using an artificial intelligence platform, the company advanced a candidate for idiopathic pulmonary fibrosis from target identification to a preclinical-ready molecule in approximately 18 months.6
Within technical development, automation supports higher-throughput experimentation and more consistent execution. Robotic systems can prepare samples, run assays, and acquire data at scales that are difficult to achieve manually. Digitalization integrates these data into accessible, structured formats that support trend analysis and knowledge reuse. Advanced analytics can then be applied to identify patterns, anticipate potential failure modes, and guide process and formulation optimization, helping teams transition from sequential trial-and-error development to more informed and parallelized approaches.7
These themes are at the heart of the symposium Transforming New Modality Drug Development through Automation, Digitalization, and AI/ML in Pittcon’s Pharmaceutical & Biologics track. Dr. Tao Chen of Genentech will discuss how high-throughput robotics and advanced analytics are reshaping the development of nucleic acid therapeutics and their delivery platforms, sharing practical insights into the challenges and opportunities of end-to-end digitalization and AI integration.
Implications for vaccines and emerging diseases
Vaccine development exemplifies both the urgency and the difficulty of accelerating timelines. Global demand for vaccines is increasing due to antimicrobial resistance, demographic shifts toward older populations, and persistent unmet needs such as malaria and HIV. Nonetheless, vaccine development has continued to be long and expensive, with high manufacturing uncertainty and a low probability of market entry for any given candidate. Technical challenges include scaling complex manufacturing processes and ensuring consistent quality and safety. Clinical trials may be limited in their ability to detect rare adverse events, and regulatory requirements are stringent for good reasons. Together, these factors result in lengthy development cycles and high employee attrition.8
Automation and digitalization can help here as well, particularly in analytical development and stability testing. This is the focus of the symposium Comprehensive Automated Solutions for Drug and Biomarker Analysis, where Dr. Weiyue Xin of Merck will describe fully automated workflows for forced degradation, high-throughput ELISA, and digital data integration, demonstrating how reproducible, scalable platforms can shorten timelines while supporting regulatory confidence.
Conclusion: Pittcon and the future of pharma technical development
Pharmaceutical technical development now sits at the boundary between increasing scientific complexity and the practical limits of time, cost, and reliability. As therapeutic platforms diversify and programs become more specific, manual and sequential approaches are no longer sufficient. Automation, digitalization, and AI/ML do not remove complexity, but they offer ways to manage it by increasing throughput, reducing variability, integrating data, and supporting more informed decisions.
Pittcon supports this transition by convening scientists and technologists who are shaping modern development workflows, providing a forum where methods, tools, and experiences can be exchanged and applied. Exhibitors such as LabWare, Inc. (Booth 1640) further demonstrate how laboratory informatics underpins the data integration required for automated and digital environments.
For those engaged in these changes, Pittcon offers both a view of what is emerging and a place to help shape it. Further information is available at Pittcon.org.
References and further reading
- IQVIA. (2025). Global Trends in R&D 2025. (online) Available at:
- Laurent, A. (2025). The Drug Development Timeline: Why It Takes Over 10 Years. (online) IntuitionLabs. Available at:
- Choi, Y., Vinks, A.A. and van (2023). Novel Therapeutic Modalities: The Future is Now. Clinical Pharmacology & Therapeutics, 114(3), pp.493–496. DOI: 10.1002/cpt.2996.
- Duarte, J.G., et al. (2025). Rethinking Pharmaceutical Industry with Quality by Design: Application in Research, Development, Manufacturing, and Quality Assurance. The AAPS Journal, 27(4). DOI: 10.1208/s12248-025-01079-w.
- Wallach, I., Dzamba, M. and Heifets, A. (2015). AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery. arXiv (Cornell University). DOI:10.48550/arxiv.1510.02855.
- Insilico. A breakthrough milestone in AI-powered drug discovery reached linking biology and chemistry with AI. (online) Available at:
- Manoj Kumar T, et al. (2024). Future of Pharmaceutical Industry: Role of Artificial Intelligence, Automation, and Robotics. Journal of pharmacology and pharmacotherapeutics, 15(2). DOI: 10.1177/0976500×241252295.
- Masignani, V., et al. (2025). De-risking vaccine development: lessons, challenges, and prospects. npj Vaccines, 10(1). DOI: 10.1038/s41541-025-01211-z.
About Pittcon
Pittcon is the world’s largest annual premier conference and exposition on laboratory science. Pittcon attracts more than 16,000 attendees from industry, academia and government from over 90 countries worldwide.
Their mission is to sponsor and sustain educational and charitable activities for the advancement and benefit of scientific endeavor.
Pittcon’s target audience is not just “analytical chemists,” but all laboratory scientists – anyone who identifies, quantifies, analyzes or tests the chemical or biological properties of compounds or molecules, or who manages these laboratory scientists.
Having grown beyond its roots in analytical chemistry and spectroscopy, Pittcon has evolved into an event that now also serves a diverse constituency encompassing life sciences, pharmaceutical discovery and QA, food safety, environmental, bioterrorism and cannabis/psychedelics.
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