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

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

Pharmaceutical technologies underpin healthcare product development. Medicinal products are becoming increasingly complex, and while the next generation of research scientists in the life- and pharmaceutical sciences will require high competency in at least one scientific discipline, they will also need to be trained differently than the current generation. Future research leaders need to be equipped with the skills required to lead innovation and change, and to work in, and connect concepts across diverse scientific disciplines and environments. This CDT will train PhD scientists in cross-disciplinary areas central to the pharmaceutical, healthcare and life sciences sectors, whilst generating impactful research in these fields. The CDT outputs will benefit the pharmaceutical and healthcare sectors and will underpin EPSRC call priorities in the development of low molecular weight molecules and biologics into high value products.

Benefits of cohort research training: The CDT's most direct beneficiaries will be the graduates themselves. They will develop cross-disciplinary scientific knowledge and expertise, and receive comprehensive soft skills training. This will render them highly employable in R&D in the pharmaceutical, healthcare and wider life-sciences sectors, as is evidenced by the employment record in R&D intensive jobs of graduates from our predecessor CDTs. Our students will graduate into a supportive network of alumni, academic, and industrial scientists, aiding them to advance their professional careers.

Benefits to industry: The pharmaceutical sector is a key part of the UK economy, and for its future success and international competitiveness a skilled workforce is needed. In particular, it urgently needs scientists trained to develop medicines from emerging classes of advanced active molecules, which have formulation requirements that are very different from current drugs. The CDT will make a considerable impact by delivering a highly educated and skilled cohort of PhD graduates. Our industrial partners include big pharma, SMEs, CROs, CMOs, CMDOs and start-up incubators, ensuring that CDT training is informed by, and our students exposed to research drivers in, a wide cross-section of industry. Research projects in the CDT will be designed through a collaborative industry-academia innovation process, bringing direct benefits to the companies involved, and will help to accelerate adoption of new science and approaches in the medicines development. Benefit to industry will also be though potential generation of IP-protected inventions in e.g. formulation materials and/or excipients with specific functionalities, new classes of drug carriers/formulations or new in vitro disease models. Both universities have proven track records in IP generation and exploitation. Given the value added by the pharma industry to the UK economy ('development and manufacture of pharmaceuticals', contributes £15.7bn in GVA to the UK economy, and supports ~312,000 jobs), the economic impacts of high-level PhD training in this area are manifest.

Benefits to society: The CDT's research into the development of new medical products will, in the longer term, deliver potent new therapies for patients globally. In particular, the ability to translate new active molecules into medicines will realise their potential to transform patient treatments for a wide spectrum of diseases including those that are increasing in prevalence in our ageing population, such as cardiovascular (e.g. hypertension), oncology (e.g. blood cancers), and central nervous system (e.g. Alzheimer's) disorders. These new medicines will also have major economic benefits to the UK. The CDT will furthermore proactively undertake public engagement activities, and will also work with patient groups both to expose the public to our work and to foster excitement in those studying science at school and inspire the next generation of research scientists.