Parkinson\'s disease (PD) is an idiopathic neurodegenerative disorder with the second-highest prevalence rate behind Alzheimer\'s disease. The pathophysiological hallmarks of PD are both degeneration of dopaminergic neurons in the substantia nigra pars compacta and the inclusion of misfolded α-synuclein (α-syn) aggregates known as Lewy bodies. Despite decades of research for potential PD treatments, none have been developed, and developing new therapeutic agents is a time-consuming and expensive process. Computational methods can be used to investigate the properties of drug candidates currently undergoing clinical trials to determine their theoretical efficiency at targeting α-syn. Monoclonal antibodies (mAbs) are biological drugs with high specificity, and Prasinezumab (PRX002) is an mAb currently in Phase II, which targets the C-terminus (AA 118-126) of α-syn. We utilized BioLuminate and PyMol for the structure prediction and preparation of the fragment antigen-binding (Fab) region of PRX002 and 34 different conformations of α-syn. Protein-protein docking simulations were performed using PIPER, and 3 of the docking poses were selected based on the best fit. Molecular dynamics simulations were conducted on the docked protein structures in triplicate for 1000 ns, and hydrogen bonds and electrostatic and hydrophobic interactions were analyzed using MDAnalysis to determine which residues were interacting and how often. Hydrogen bonds were shown to form frequently between the HCDR2 region of PRX002 and α-syn. Free energy was calculated to determine the binding affinity. The predicted binding affinity shows a strong antibody-antigen attraction between PRX002 and α-syn. RMSD was calculated to determine the conformational change of these regions throughout the simulation. The mAb\'s developability was determined using computational screening methods. Our results demonstrate the efficiency and developability of this therapeutic agent.

We previously described an in vitro single-chain fragment (scFv) library platform originally designed to generate antibodies with excellent developability properties. The platform design was based on the use of clinical antibodies as scaffolds into which replicated natural complementarity-determining regions purged of sequence liabilities were inserted, and the use of phage and yeast display to carry out antibody selection. In addition to being developable, antibodies generated using our platform were extremely diverse, with most campaigns yielding sub-nanomolar binders. Here, we describe a platform advancement that incorporates Fab phage display followed by single-chain antibody-binding fragment Fab (scFab) yeast display. The scFab single-gene format provides balanced expression of light and heavy chains, with enhanced conversion to IgG, thereby combining the advantages of scFvs and Fabs. A meticulously engineered, quality-controlled Fab phage library was created using design principles similar to those used to create the scFv library. A diverse panel of binding scFabs, with high conversion efficiency to IgG, was isolated against two targets. This study highlights the compatibility of phage and yeast display with a Fab semi-synthetic library design, offering an efficient approach to generate drug-like antibodies directly, facilitating their conversion to potential therapeutic candidates.

Bispecific antibodies (bsAb) and multispecific antibodies (msAb) encompass a diverse variety of formats that can concurrently bind multiple epitopes, unlocking mechanisms to address previously difficult-to-treat or incurable diseases. Early assessment of candidate developability enables demotion of antibodies with low potential and promotion of the most promising candidates for further development. Protein-based therapies have a stringent set of developability requirements in order to be competitive (e.g. high-concentration formulation, and long half-life) and their assessment requires a robust toolkit of methods, few of which are validated for interrogating bsAbs/msAbs. Important considerations when assessing the developability of bsAbs/msAbs include their molecular format, likelihood for immunogenicity, specificity, stability, and potential for high-volume production. Here, we summarize the critical aspects of developability assessment, and provide guidance on how to develop a comprehensive plan tailored to a given bsAb/msAb.

The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success and ensure robust drug supply beyond approval, it is essential to assess the developability profile of new potential drug candidates as early and broadly as possible in development (Jain et al. MAbs, 2023. https://doi.org/10.1016/j.copbio.2011.06.002 ). Predicting these properties in silico is expected to be the next leap in innovation as it would enable significantly reduced development timelines combined with broader screens at lower costs. However, developing predictive algorithms typically requires substantial datasets generated under very defined conditions, a limiting factor especially for new classes of therapeutic proteins that hold immense clinical promise. Here we describe a strategy for assessing the developability of a novel class of small therapeutic Anticalin® proteins using machine learning in conjunction with a knowledge-driven approach. The knowledge-driven approach considers developability attributes such as aggregation propensity, charge variants, immunogenicity, specificity, thermal stability, hydrophobicity, and potential post-translational modifications, to calculate a holistic developability score. Based on sequence-derived descriptors as input parameters we established novel statistical models designed to predict the developability scores for Anticalin proteins. The best models yielded low root mean square errors across the entire dataset and were further validated by removing input data from individual screening campaigns and predicting developability scores for those drug candidates. The adoption of the described workflow will enable significantly streamlined preclinical development of Anticalin drug candidates and could potentially be applied to other therapeutic protein scaffolds.

In vitro assessments for the prediction of pharmacokinetic (PK) behavior of biotherapeutics can help identify corresponding liabilities significantly earlier in the discovery timeline. This can minimize the need for extensive early in vivo PK characterization, thereby reducing animal usage and optimizing resources. In this study, we recommend bolstering classical developability workflows with in vitro measures correlated with PK. In agreement with current literature, in vitro measures assessing nonspecific interactions, self-interaction, and FcRn interaction are demonstrated to have the highest correlations to clearance in hFcRn Tg32 mice. Crucially, the dataset used in this study has broad sequence diversity and a range of physicochemical properties, adding robustness to our recommendations. Finally, we demonstrate a computational approach that combines multiple in vitro measurements with a multivariate regression model to improve the correlation to PK compared to any individual assessment. Our work demonstrates that a judicious choice of high throughput in vitro measurements and computational predictions enables the prioritization of candidate molecules with desired PK properties.

The manufacturability assessment and optimization of bispecific antibodies (bsAbs) during the discovery stage are crucial for the success of the drug development process, impacting the speed and cost of advancing such therapeutics to the Investigational New Drug (IND) stage and ultimately to the market. The complexity of bsAbs creates challenges in employing effective evaluation methods to detect developability risks in early discovery stage, and poses difficulties in identifying the root causes and implementing subsequent engineering solutions. This study presents a case of engineering a bsAb that displayed a normal solution appearance during the discovery phase but underwent significant precipitation when subjected to agitation stress during 15 L Chemistry, Manufacturing, and Control (CMC) production Leveraging analytical tools, structural analysis, in silico prediction, and wet-lab validations, the key molecular origins responsible for the observed precipitation were identified and addressed. Sequence engineering to reduce protein surface hydrophobicity and enhance conformational stability proved effective in resolving agitation-induced aggregation. The refined bsAb sequences enabled successful mass production in CMC department. The findings of this case study contribute to the understanding of the fundamental mechanism of agitation-induced aggregation and offer a potential protein engineering procedure for addressing similar issues in bsAb. Furthermore, this case study emphasizes the significance of a close partnership between Discovery and CMC teams. Integrating CMC\'s rigorous evaluation methods with Discovery\'s engineering capability can facilitate a streamlined development process for bsAb molecules.

Single-domain antibodies (sdAbs), such as VHHs, are increasingly being developed for gastrointestinal (GI) applications against pathogens to strengthen gut health. However, what constitutes a suitable developability profile for applying these proteins in a gastrointestinal setting remains poorly explored. Here, we describe an in vitro methodology for the identification of sdAb derivatives, more specifically divalent VHH constructs, that display extraordinary developability properties for oral delivery and functionality in the GI environment. We showcase this by developing a heterodivalent VHH construct that cross-inhibits the toxic activity of the glycosyltransferase domains (GTDs) from three different toxinotypes of cytotoxin B (TcdB) from lineages of Clostridium difficile. We show that the VHH construct possesses high stability and binding activity under gastric conditions, in the presence of bile salts, and at high temperatures. We suggest that the incorporation of early developability assessment could significantly aid in the efficient discovery of VHHs and related constructs fit for oral delivery and GI applications.

Supersaturation and precipitation within the gastrointestinal tract can influence oral absorption of active pharmaceutical ingredients (APIs). Supersaturation of weakly basic APIs upon transfer from the stomach into the small intestine may enhance their absorption, while salt forms of poorly soluble weak acids may generate supersaturated solutions in both stomach and intestine. Likewise, APIs with solubility-limited absorption may be developed as enabling formulations intended to produce supersaturated solutions of the API in the gut. Integrating the supersaturation/precipitation characteristics of the API into the biopharmaceutical risk classification enables comprehensive mapping of potential developability risks and guides formulation selection towards optimizing oral bioavailability (BA). The refined Developability Classification System (rDCS) provides an approach for this purpose. In this work, the rDCS strategy is revisited and a stratified approach integrating the in vitro supersaturation and precipitation behavior of APIs and their formulations is proposed.

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.

The formulation of high-concentration monoclonal antibody (mAb) solutions in low dose volumes for autoinjector devices poses challenges in manufacturability and patient administration due to elevated solution viscosity. Often many therapeutically potent mAbs are discovered, but their commercial development is stalled by unfavourable developability challenges. In this work, we present a systematic experimental framework for the computational screening of molecular descriptors to guide the design of 24 mutants with modified viscosity profiles accompanied by experimental evaluation. Our experimental observations using a model anti-IL8 mAb and eight engineered mutant variants reveal that viscosity reduction is influenced by the location of hydrophobic interactions, while targeting positively charged patches significantly increases viscosity in comparison to wild-type anti-IL-8 mAb. We conclude that most predicted in silico physicochemical properties exhibit poor correlation with measured experimental parameters for antibodies with suboptimal developability characteristics, emphasizing the need for comprehensive case-by-case evaluation of mAbs. This framework combining molecular design and triage via computational predictions with experimental evaluation aids the agile and rational design of mAbs with tailored solution viscosities, ensuring improved manufacturability and patient convenience in self-administration scenarios.