Small molecules as therapeutic agents have played a significant role in the improvement of human health and wellbeing since the discovery and commercialisation of molecules such as Aspirin and Salvarsan over a century ago.1 As the population ages, human behaviors shift and lifestyles change, and so do the demands for effective treatments.
These changes pose several challenges. As humans live longer, neurodegenerative conditions pose an increased burden, requiring effective treatment.2 The need for continued evolution within the field of cancer research is also well cited, given the rank diversity of the disease. Small molecules continue to play a significant role in treating a wide variety of indications.
Historically, it has been estimated that approximately 90% of all marketed drugs are represented by small molecules, demonstrating the value and opportunities that these drug candidates provide.3 The societal changes and advancements in technologies available to scientists have opened the door to the generation of new leads in the small molecule field. Drug developers and manufacturers are navigating this challenge using in silico screening tools in combination with artificial intelligence (AI) in the form of machine learning algorithms. These aim to significantly reduce the risk of failure and streamline the development process, to provide structures of stable, druggable target molecules that are sensible from both a synthetic and a toxicological perspective.4,5
Although this is an exciting advancement in the complex process of developing new medicines, there are an increasing number of small molecules entering the development pipeline that exhibit significant challenges to their progression. These most commonly manifest as sub-optimal physicochemical characteristics such as very low solubility, poor permeability, and unacceptable powder handling properties. As such, the need for the integration of more traditional medicinal chemistry and formulation expertise remains.
An Increase in Challenging Small Molecules in the Pipeline
The lead optimisation process while targeting increased potency often increases molecular weight, the number of hydrogen bond donors, acceptors, and lipophilicity (LogP). This in turn deteriorates the druglike properties of the molecule, reducing solubility and permeability. Whether the fragment-based “rule of three” or Lipinski’s rule of five is applied during the optimisation process, the fact remains that suboptimal properties are one of the biggest challenges facing development groups. It’s important to note that as with all rules druggable space does exist beyond the rule of five.7 This is why a close integration of the groups involved in the development process is strategically beneficial.
One of the benefits of integration is access to material. If the composition of the final product is brought to the forefront of the initial interactions between the chemist and the solid-state scientist, early batches can be profiled with only a few mg cost in terms of spent active pharmaceutical ingredient (API). Additionally, the behavior of the API and its propensity to change as the process is optimised – be that solvation, polymorphic form, or amorphous characteristics –, can be readily communicated between groups. This can help to form the foundations of future development.