Stabilizing therapeutic protein solutions: Optimisation and Evaluation of Excipient Properties using MD, QSAR and Synthesis

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Aggregation of therapeutic proteins including antibodies has been identified as a major challenge to their commercialisation and clinical use. Aggregation can cause reduced biological activity, increased viscosity and potentially enhanced immunogenicity. These issues have resulted in the employment of several types of excipients in current therapeutic protein formulations (Humira (adalimumab) contains 0.1% W/V polysorbate 80 (TWEEN80), Raptiva (efalizumab) 0.2% W/V polysorbate 20 (TWEEN20), Orencia (abatacept) contains poloxamer 188 (pluronic F-68). These non- ionic surfactants have been chosen mainly because they have well established safety profiles, rather than outstanding performance as protein stabilizing agents.The proposed research will have two main activities. Using existing excipient structures with known properties and literature data, a combination of in silico molecular dynamics and where sufficient related excipient structures have been studied, in silico QSAR studies incorporating machine learning will be undertaken.Using techniques we have established through previous CDT projects (see Mackenzie, JCTC 2015, 11, 2705-2713) molecular dynamics simulations will study the interactions between selected excipients and proteins with established 3-dimensional structure. These will characterise the locations, strengths, and (possibly) timescales of interactions, and the effects that excipient interactions haver on the structure of the protein.Machine learning methods will be used to model the relationships between chemical structures of the excipients and their protein binding affinities. A variety of 2D- and 3D-representations will be used to describe the chemical structures. Graph-based approaches capture the connectivity of different atom types and are quick to compute and readily generalizable. Interaction fields are derived from the 3D structures of the molecules and can be extended to incorporate conformational sampling. These representations will be used to train machine learning methods, including support vector machines, neural networks and random forests. New experimental data will be used to refine the machine learning methods, increasing their predictive ability. Conversely, the models will be used to guide subsequent experiments, in order to test specific hypotheses about the importance of various physicochemical properties and to identify more effective excipients.The results of these studies will inform and guide the development of new protein stabilization excipients, both through moderate modifications such as homologation/monomer extension of existing surfactants and more disruptive changes such as the inclusion of new functional groups that can change the LogP/LogD; rotational freedom (i.e addition of E/Z alkenes or cyclopropyl/diol groups to unsaturated ); H-bonding ability; -stacking ability; or inclusion of charged groups such as the guanidine group as found in arginine (an excipient that can ion-pair, H-bond with carboxylate groups and form -cation interactions with aromatic groups). The ability of both existing and new compounds to stabilize a range of therapeutic proteins (insulin, abatacept, human serum albumin, adalimumab) in solution will then be studied using a manifold of biophysical techniques (CD, ITC, SEC, DLS, AUC) in order to determine which has the largest stabilizing effect, and to quantify the surfactant structure and activity.The student will therefore be trained in a range of complementary techniques including computational methods, organic synthesis and compound characterization and a range of biophysical techniques for characterizing protein-excipient mixtures. This project fits within the 21st Century Products priority, Healthcare Technologies (developing future therapies) and manufacturing for the future themes of the EPSRC.Project aligned to Predictive Pharmaceutical Sciences, Advanced Product Design and Complex Product Characterisation