The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.
Therapeutic antibodies are one of the most important parts of the pharmaceutical industry. They are widely used in treating various diseases such as autoimmune diseases, cancer, inflammation, and infectious diseases. Their development process however is often brought to a standstill or takes a longer time and is then more expensive due to their hydrophobicity problems. Hydrophobic interactions can cause problems on half-life, drug administration, and immunogenicity at all stages of antibody drug development. Some of the most widely accepted and used technologies for determining the hydrophobic interactions of antibodies include standup monolayer adsorption chromatography (SMAC), salt-gradient affinity-capture self-interaction nanoparticle spectroscopy (SGAC-SINS), and hydrophobic interaction chromatography (HIC). However, to measure SMAC, SGAC-SINS, and HIC for hundreds of antibody drug candidates is time-consuming and costly. To save time and money, a predictor called SSH is developed. Based on the antibody’s sequence only, it can predict the hydrophobic interactions of monoclonal antibodies (mAbs). Using the leave-one-out crossvalidation, SSH achieved 91.226% accuracy, 96.396% sensitivity or recall, 84.196% specificity, 87.754% precision, 0.828 Mathew correlation coefficient (MCC), 0.919 f-score, and 0.961 area under the receiver operating characteristic (ROC) curve (AUC).
Background
Over 120 antibody-based therapeutics have been approved by FDA for the treatments of cancers, immune-related diseases, infectious disease and hematological disease, etc. However, the development of therapeutic antibody is full of challenge that candidates could fail because of unfavourable physicochemical properties. Light chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with less amyloidosis risk at early stage can not only save the time and cost of antibody development, but also improve the safety of antibody drugs.
Methods
In this study, we build a data set of the sequences of 742 amyloidogenic antibody light chains and 712 non-amyloidogenic antibody light chains. Based on dipeptide composition of these data, a support vector machine (SVM)-based model, AB-Amy, was trained to predict the light chain amyloidogenic risk.
Results
On an independent test data set, the sensitivity, specificity, ACC, MCC and AUC of AB-Amy reach 93.80%, 91.98%, 92.95%, 0.8584 and 0.9651 respectively. A web server was also built for AB-Amy, freely available at http://i.uestc.edu.cn/AB-Amy/. Compared with existing methods, our proposed model shows superior performance.
Conclusions
AB-Amy can be a useful tool for in silico evaluation of the light chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development.
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