PhD ThesisMonoclonal antibodies (mAbs) and related therapeutics are highly desirable from a
biopharmaceutical perspective as they are highly target specific and well tolerated within the
human system. Nevertheless, several mAbs have been discontinued or withdrawn based either
on their inability to demonstrate efficacy and/or due to adverse effects. With nearly 80% of
drugs failing in clinical development mainly due to lack of efficacy and safety there arises an
urgent need for better understanding of biological activity, affinity, pharmacology, toxicity,
immunogenicity etc. thus leading to early prediction of success/failure. In this study a hybrid
modelling framework was developed that enabled early stage screening of mAbs. The
applicability of the experimental methods was first tested on chemical compounds to assess the
assay quality following which they were used to assess potential off target adverse effects of
mAbs. Furthermore, hypersensitivity reactions were assessed using Skimune™, a non-artificial
human skin explants based assay for safety and efficacy assessment of novel compounds and
drugs, developed by Alcyomics Ltd. The suitability of Skimune™ for assessing the immune
related adverse effects of aggregated mAbs was studied where aggregation was induced using
a heat stress protocol. The aggregates were characterised by protein analysis techniques such
as analytical ultra-centrifugation following which the immunogenicity tested using Skimune™
assay. Numerical features (descriptors) of mAbs were identified and generated using ProtDCal,
EMBOSS Pepstat software as well as amino acid scales for different. Five independent and
novel X block datasets consisting of these descriptors were generated based on the
physicochemical, electronic, thermodynamic, electronic and topological properties of amino
acids: Domain, Window, Substructure, Single Amino Acid, and Running Sum. This study
describes the development of a hybrid QSAR based model with a structured workflow and clear
evaluation metrics, with several optimisation steps, that could be beneficial for broader and
more generic PLS modelling. Based on the results and observation from this study, it was
demonstrated incremental improvement via selection of datasets and variables help in further
optimisation of these hybrid models. Furthermore, using hypersensitivity and cross reactivity
as responses and physicochemical characteristics of mAbs as descriptors, the QSAR models
generated for different applicability domains allow for rapid early stage screening and
developability. These models were validated with external test set comprising of proprietary
compounds from industrial partners, thus paving way for enhanced developability that tackles
manufacturing failures as well as attrition rates.European Union’s
Horizon 2020 research and innovation program under the Marie Skłodowska-Curie actions
grant agreemen