Best Practices for Solving Complex Formulation Challenges While Maintaining Speed Through the Lifecycle

Summary: In this Pharma Matters Q&A, challenges of complex formulations are tackled head-on.

Formulation complexity, especially in small molecule drugs, has been getting more attention as research and technological capabilities continue to improve.

Yet even as these formulations hold potential for more targeted delivery, challenges remain in solubility and scale-up.

For this Pharma Matters Q&A, Asma Patel, Vice President, Global Commercial and Scientific Consulting—Drug Product at Quotient Sciences, takes the journey from development to the commercial stage. Patel also stops along the way to address the biggest challenges in the process. Finally, she provides a roadmap for keeping robust speed to clinic while focusing on efficiency and reducing risk.

Contract Pharma: Complex small molecule formulations are gaining momentum right now. However, these present challenges in factors like solubility, bioavailability, and ultimately, delivery. In solving these and other issues, what have you seen that is working? What’s been less successful?

Asma Patel: Addressing solubility and bioavailability challenges in complex small molecules is most effective when formulation design is tightly coupled to in vivo performance, rather than relying on in vitro prediction models alone. Integrated approaches such as Quotient Sciences Translational Pharmaceutics® enable the rapid clinical evaluation of multiple formulation strategies. These include particle size reduction, lipid based systems, and amorphous solid dispersions. All of these provide direct pharmacokinetic (PK) insight into exposure, variability, and absorption mechanisms. This allows selection of formulations that not only achieve target bioavailability but also demonstrate acceptable dose proportionality, stability, and manufacturability.

Critically, this approach supports identification of formulations that can be quickly scaled and economically produced, minimizing downstream cost of goods. In contrast, development approaches using in vitro screening, or hypotheses that are not clinically verified, frequently cause suboptimal formulation selection. This often brings about re‑optimization during late stage clinical development. Delays, increased costs, and challenges in satisfying commercial manufacturing constraints can result.  

CP: Even with complex formulations, an urgency remains to move along processes faster, with as few delays as possible. What innovations or technologies are proving useful right now in that regard?

Patel: The innovations that have highest impact are those that integrate biopharmaceutics, formulation science, and clinical pharmacology into a unified decision making framework. Platforms that combine GMP manufacturing with real time clinical evaluation enable rapid iteration of formulation prototypes. This allows direct assessment of dissolution–absorption relationships, variability, or food effects in humans. The ability to judge multiple formulations within a single clinical study creates a powerful feedback loop. Accordingly, the pace that identification of the optimal balance between solubility enhancement strategy, systemic exposure, and dose feasibility speeds up.

These experimental approaches are increasingly improved through digital innovation. Quotient Sciences’ collaboration with Intrepid Labs brings the ANDROMEDA™ machine learning platform into formulation development. The platform enables rapid exploration of formulation design space and prediction of clinical outcomes. By joining empirical clinical data with predictive models, this approach supports more informed, data rich selection of formulation strategies. In turn, this improves confidence in early decision making while reducing development iteration cycles.

Together, the integration of clinically informed formulation development, flexible manufacturing, and AI‑enabled design makes faster, more confident decision making possible. Importantly, it also ensures that selected formulations are technically transferrable, reproducible at scale, and commercially viable, reducing late stage disruption and encouraging a smoother transition from early development through to commercial supply.

CP: That said, going from the earliest stages of development to commercial stage takes time and has numerous issues. Among those are tech transfer, scale-up, and batch consistency. How do you go about cutting back pain points like these? What about if or when they threaten to cause delays?

Patel: Mitigating risks associated with scale-up and technical transfer requires early establishment of a robust understanding of the relationship between formulation composition, process parameters, and in vivo performance. Integrated development models reduce the need for traditional inter-organizational tech transfer, saving critical process knowledge and consistently applying control strategies across scales.

By gaining clinically relevant PK data early in development, it becomes possible to define critical quality attributes and link them directly to clinical performance, thereby making the scientific basis for scale-up decisions stronger. This facilitates the development of processes that are usually more reproducible and less sensitive to scale dependent variables.

As a result, scale-up to late stage and commercial batch sizes becomes more predictable. This supports process validation and regulatory approval. Where challenges do arise, the ability to rapidly re‑evaluate formulation or process adjustments in a clinical context helps maintain the development clock and prevents costly delays to commercial readiness.  

CP: Dovetailing with the potential of delays, speed to clinic and/or proof of concept are always top concerns. What are the best ways to ensure efficiency while reducing risk?

Patel: Achieving both speed and risk reduction requires an approach placing early clinical insight over iterative preclinical optimization. Integrated CRDMO models enable just in time manufacturing of drug product, reducing API consumption while being able to support rapid movement to first in human and subsequent optimization studies. This is particularly important for compounds with suboptimal PK profiles, such as low bioavailability, high inter-subject variability, or dose limiting Cmax-related adverse events. In these scenarios, the ability to evaluate modified release or enabling formulation technologies within a clinical setting provides direct evidence to guide dose regimen and formulation selection.

By solving these uncertainties early, development programs can converge on formulations that are not only clinically effective but can also work well with late stage manufacturing requirements, including process scalability, supply chain robustness, and cost efficiency. This approach reduces the likelihood of reformulation during pivotal studies and supports a smooth transition into commercial production.

Ultimately, the most successful strategies are those that treat clinical performance, process design, and commercial viability as interdependent variables that enable the selection of formulations that are scientifically robust, operationally scalable, and economically sustainable through to launch.