Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

An antibody developability triaging pipeline exploiting protein language models

Therapeutic monoclonal antibodies (mAbs) are a successful class of biologic drugs that are frequently selected from phage display libraries and transgenic mice that produce fully human antibodies. However, binding affinity to the correct epitope is necessary, but not sufficient, for a mAb to have therapeutic potential. Sequence and structural features affect the developability of an antibody, which influences its ability to be produced at scale and enter trials, or can cause late-stage failures. Using data on paired human antibody sequences, we introduce a pipeline using a machine learning approach that exploits protein language models to identify antibodies which cluster with antibodies that have entered the clinic and are therefore expected to have developability features similar to clinically acceptable antibodies, and triage out those without these features. We propose this pipeline as a useful tool in candidate selection from large libraries, reducing the cost of exploration of the antibody space, and pursuing new therapeutics.

Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt

In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalizability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported. Here, we present a comprehensive study exploring various approaches for protein fitness prediction from limited data, leveraging pre-trained embeddings, repeated stratified nested cross-validation, and ensemble learning to ensure an unbiased assessment of the performances. We applied our framework to introduce NanoMelt, a predictor of nanobody thermostability trained with a dataset of 640 measurements of apparent melting temperature, obtained by integrating data from the literature with 129 new measurements from this study. We find that an ensemble model stacking multiple regression using diverse sequence embeddings achieves state-of-the-art accuracy in predicting nanobody thermostability. We further demonstrate NanoMelt's potential to streamline nanobody development by guiding the selection of highly stable nanobodies. We make the curated dataset of nanobody thermostability freely available and NanoMelt accessible as a downloadable software and webserver.

Understanding IgM Structure and Biology to Engineer New Antibody Therapeutics

Immunoglobulin M (IgM) antibodies are an essential and conserved part of adaptive immunity. IgMs assemble into pentamers and hexamers that bind to antigens with high avidity. Pentamers incorporate a small protein called J-chain (JC) that is important for their transcytosis via the poly-immunoglobulin receptor (pIgR). IgM antibodies can efficiently activate complement and interact with different Fc receptors (FcμR, Fcα/μR, pIgR) that trigger distinct effector functions and biodistribution. Even if these features have made the clinical use of IgM attractive over the past decades, there are currently no approved therapeutic IgMs on the market. In this review, we summarize the recent advances in the knowledge of IgM biogenesis and structure and discuss the therapeutic opportunities of IgM over IgG arising from high avidity, target clustering, binding to distinct IgM receptors, complement activation, transcytosis, and protein engineering opportunities. In addition, we summarize possibilities and outstanding challenges in the production of therapeutic IgM, including available technologies for IgM purification. Finally, we review recent preclinical and clinical data showing that IgM outperforms IgG in various in vitro assays but still fails to pass through clinical trials successfully. Challenges remain for IgM development, such as the need for a better understanding of IgM biology to facilitate a smoother transition from the preclinic to successful clinical trials.

Trends and challenges in bispecific antibody production

Bispecific antibodies (bsAbs) represent a rapidly growing field of therapeutic agents. More bsAbs are being approved worldwide and are in various stages of clinical trials. However, the discovery and production of novel bsAbs presents significant challenges due to their complex structure. Thus, precise control of assembly and stability is required, given the many formats developed. This review examines recent trends in bsAb production, focusing on advancements in engineering platforms, production strategies, and challenges in large-scale manufacturing. Key developments include improvements in modular antibody design, novel expression systems, and optimization of bioprocessing techniques to enhance stability, yield, and efficacy. Additionally, the article explores the future potential of bsAbs as next-generation therapeutics, underscoring the growing impact of these innovations on expanding treatment options for patients with unmet medical needs.

Methodological and Ethical Considerations in the Use of Chordate Embryos in Biomedical Research

Animal embryos are vital tools in scientific research, providing insights into biological processes and disease mechanisms. This paper explores their historical and contemporary significance, highlighting the shift towards the refinement of in vitro systems as alternatives to animal experimentation. We have conducted a data review of the relevant literature on the use of embryos in research and synthesized the data to highlight the importance of this model for scientific progress and the ethical considerations and regulations surrounding embryo research, emphasizing the importance of minimizing animal suffering while promoting scientific progress through the principles of replacement, reduction, and refinement. Embryos from a wide range of species, including mammals, fish, birds, amphibians, and reptiles, play a crucial experimental role in enabling us to understand factors such as substance toxicity, embryonic development, metabolic pathways, physiological processes, etc., that contribute to the advancement of the biological sciences. To apply this model effectively, it is essential to match the research objectives with the most appropriate methodology, ensuring that the chosen approach is appropriate for the scope of the study.

Unlocking the potential of in silico approach in designing antibodies against SARS-CoV-2

Antibodies are naturally produced safeguarding proteins that the immune system generates to fight against invasive invaders. For centuries, they have been produced artificially and utilized to eradicate various infectious diseases. Given the ongoing threat posed by COVID-19 pandemics worldwide, antibodies have become one of the most promising treatments to prevent infection and save millions of lives. Currently, in silico techniques provide an innovative approach for developing antibodies, which significantly impacts the formulation of antibodies. These techniques develop antibodies with great specificity and potency against diseases such as SARS-CoV-2 by using computational tools and algorithms. Conventional methods for designing and developing antibodies are frequently costly and time-consuming. However, in silico approach offers a contemporary, effective, and economical paradigm for creating next-generation antibodies, especially in accordance with recent developments in bioinformatics. By utilizing multiple antibody databases and high-throughput approaches, a unique antibody construct can be designed in silico, facilitating accurate, reliable, and secure antibody development for human use. Compared to their traditionally developed equivalents, a large number of in silico-designed antibodies have advanced swiftly to clinical trials and became accessible sooner. This article helps researchers develop SARS-CoV-2 antibodies more quickly and affordably by giving them access to current information on computational approaches for antibody creation.

A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles

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.

Fit-for-purpose heterodivalent single-domain antibody for gastrointestinal targeting of toxin B from Clostridium difficile

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.

A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage and its implications

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.

ProtT5 and random forests-based viscosity prediction method for therapeutic mAbs

Viscosity is a key characteristic of therapeutic antibodies for subcutaneous administration which requires low volume and high concentration formulations. It would be highly beneficial to accurately predict the viscosity of newly developed therapeutic antibodies in the early stages of development. In this work, a ProtT5-XL-UniRef50 (ProtT5) and Random Forests (RF)-based prediction method was proposed for accurately predicting the viscosity of monoclonal antibodies, with only corresponding sequences needed. Starting from the given heavy and light chain V-region sequences, corresponding features were first extracted from the ProtT5 pretrained model. Kernel principal analysis (Kernel-PCA) was then used for reducing the extracted 2048-D (1024-D for each sequence) feature vector to a reasonable level for efficient training of the RF-regressor. Then, the RF model was constructed on 40 commercially available therapeutic antibodies and tested with 3-folds cross-validation. Test results show that the model could reproduce the viscosity value at a high level (Pearson correlation coefficient (PCC) = 0.928). Performance on classifying high (>30 cP) and low (<30 cP) viscosity is much more satisfactory, the Accuracy (ACC) and the area under precision-recall curve (AUC) of the classification model from validation tests are 0.975 and 1.000, respectively. Compared to 5 existing state-of-the-art viscosity prediction methods, the proposed method performs best which would facilitate high concentration antibody viscosity high-throughput screening.

A comparative study of the developability of full-length antibodies, fragments, and bispecific formats reveals higher stability risks for engineered constructs

Engineered antibody formats, such as antibody fragments and bispecifics, have the potential to offer improved therapeutic efficacy compared to traditional full-length monoclonal antibodies (mAbs). However, the translation of these non-natural molecules into successful therapeutics can be hampered by developability challenges. Here, we systematically analyzed 64 different antibody constructs targeting Tumor Necrosis Factor (TNF) which cover 8 distinct molecular format families, encompassing full-length antibodies, various types of single chain variable fragments, and bispecifics. We measured 15 biophysical properties related to activity, manufacturing, and stability, scoring variants with a flag-based risk approach and a recent in silico developability profiler. Our comparative assessment revealed that overall developability is higher for the natural full-length antibody format. Bispecific antibodies, antibodies with scFv fragments at the C-terminus of the light chain, and single-chain Fv antibody fragments (scFvs) have intermediate developability properties, while more complicated formats, such as scFv- scFv, bispecific mAbs with one Fab exchanged with a scFv, and diabody formats are collectively more challenging. In particular, our study highlights the propensity for fragmentation and aggregation, both in bulk and at interfaces, for many current engineered formats.

Variable domain mutational analysis to probe the molecular mechanisms of high viscosity of an IgG1 antibody

Subcutaneous injection is the preferred route of administration for many antibody therapeutics for reasons that include its speed and convenience. However, the small volume limit (typically ≤ 2 mL) for subcutaneous delivery often necessitates antibody formulations at high concentrations (commonly ≥100 mg/mL), which may lead to physicochemical problems. For example, antibodies with large hydrophobic or charged patches can be prone to self-interaction giving rise to high viscosity. Here, we combined X-ray crystallography with computational modeling to predict regions of an anti-glucagon receptor (GCGR) IgG1 antibody prone to self-interaction. An extensive mutational analysis was undertaken of the complementarity-determining region residues residing in hydrophobic surface patches predicted by spatial aggregation propensity, in conjunction with residue-level solvent accessibility, averaged over conformational ensembles from molecular dynamics simulations. Dynamic light scattering (DLS) was used as a medium throughput screen for self-interaction of ~ 200 anti-GCGR IgG1 variants. A negative correlation was found between the viscosity determined at high concentration (180 mg/mL) and the DLS interaction parameter measured at low concentration (2-10 mg/mL). Additionally, anti-GCGR variants were readily identified with reduced viscosity and antigen-binding affinity within a few fold of the parent antibody, with no identified impact on overall developability. The methods described here may be useful in the optimization of other antibodies to facilitate their therapeutic administration at high concentration.

Assessment and incorporation of in vitro correlates to pharmacokinetic outcomes in antibody developability workflows

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.

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers

The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.

Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics

In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.

Automated optimisation of solubility and conformational stability of antibodies and proteins

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.

Multidimensional Protein Solubility Optimization with an Ultrahigh-Throughput Microfluidic Platform

Protein-based biologics are highly suitable for drug development as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to elevated concentrations, making insufficient solubility a critical bottleneck in the drug development pipeline. Here, we report an ultrahigh-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 μg of protein (>10-fold improvement), and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation excipients, such as sodium chloride, polysorbate, histidine, arginine, and sucrose. Additionally, we can measure how solubility is affected by the combinatorial effect of multiple additives, find a suitable pH for the formulation, and measure the impact of mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multidimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics

Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.

AF4 and PEG Precipitation as Predictive Assays for Antibody Self-Association

Monoclonal antibodies (mAbs) are often formulated as high-protein-concentration solutions, which in some cases can exhibit physical stability issues such as high viscosity and opalescence. To ensure that mAb-based drugs can meet their manufacturing, stability, and delivery requirements, it is advantageous to screen for and select mAbs during discovery that are not prone to such behaviors. It has been recently shown that both these macroscopic properties can be predicted to a certain extent from the diffusion interaction parameter (k_D), which is a measure of self-association under dilute conditions.¹ However, k_D can be challenging to measure at the early stage of discovery, where a relatively large amount of a high-purity material, which is required by traditional methods, is often not available. In this study, we demonstrate asymmetric field-flow fractionation (AF4) as a tool to measure self-association and therefore identify antibodies with problematic issues at high concentrations. The principle lies on the ability to concentrate the sample close to the membrane during the injection mode, which can reach formulation-relevant concentrations (>100 mg/mL).² By analyzing a well-characterized library of commercial antibodies, we show that the measured retention time of the antibodies allows us to pinpoint molecules that exhibit issues at high concentrations. Remarkably, our AF4 assay requires very little (30 μg) sample under dilute conditions and does not need extensive sample purification. Furthermore, we show that a polyethylene glycol (PEG) precipitation assay provides results consistent with AF4 and moreover can further differentiate molecules with issues of opalescence or high viscosity. Overall, our results delineate a two-step strategy for the identification of problematic variants at high concentrations, with AF4 for early developability screening, followed by a PEG assay to validate the problematic molecules and further discriminate between opalescence or high-viscosity issues. This two-step antibody selection strategy enables us to select antibodies early in the discovery process, which are compatible with high-concentration formulations.

Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties

Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.

Fragment-based computational design of antibodies targeting structured epitopes

De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes.

Conformational Entropy as a Potential Liability of Computationally Designed Antibodies

In silico antibody discovery is emerging as a viable alternative to traditional in vivo and in vitro approaches. Many challenges, however, remain open to enabling the properties of designed antibodies to match those produced by the immune system. A major question concerns the structural features of computer-designed complementarity determining regions (CDRs), including the role of conformational entropy in determining the stability and binding affinity of the designed antibodies. To address this problem, we used enhanced-sampling molecular dynamics simulations to compare the free energy landscapes of single-domain antibodies (sdAbs) designed using structure-based (DesAb-HSA-D3) and sequence-based approaches (DesAbO), with that of a nanobody derived from llama immunization (Nb10). Our results indicate that the CDR3 of DesAbO is more conformationally heterogeneous than those of both DesAb-HSA-D3 and Nb10, and the CDR3 of DesAb-HSA-D3 is slightly more dynamic than that of Nb10, which is the original scaffold used for the design of DesAb-HSA-D3. These differences underline the challenges in the rational design of antibodies by revealing the presence of conformational substates likely to have different binding properties and to generate a high entropic cost upon binding.

Functional and structural analysis of non-synonymous single nucleotide polymorphisms (nsSNPs) in the MYB oncoproteins associated with human cancer

MYB proteins are highly conserved DNA-binding domains (DBD) and mutations in MYB oncoproteins have been reported to cause aberrant and augmented cancer progression. Identification of MYB molecular biomarkers predictive of cancer progression can be used for improving cancer management. To address this, a biomarker discovery pipeline was employed in investigating deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in predicting damaging and potential alterations on the properties of proteins. The nsSNP of the MYB family; MYB, MYBL1, and MYBL2 was extracted from the NCBI database. Five in silico tools (PROVEAN, SIFT, PolyPhen-2, SNPs&GO and PhD-SNP) were utilized to investigate the outcomes of nsSNPs. A total of 45 nsSNPs were predicted as high-risk and damaging, and were subjected to PMut and I-Mutant 2.0 for protein stability analysis. This resulted in 32 nsSNPs with decreased stability with a DDG score lower than - 0.5, indicating damaging effect. G111S, N183S, G122S, and S178C located within the helix-turn-helix (HTH) domain were predicted to be conserved, further posttranslational modifications and 3-D protein analysis indicated these nsSNPs to shift DNA-binding specificity of the protein thus altering the protein function. Findings from this study would help in the field of pharmacogenomic and cancer therapy towards better intervention and management of cancer.