Reshaping the pharmaceutical industry: Enter AI
Biologics like monoclonal antibodies are much larger than small molecule drugs like Aspirin. Source: Food and Drug Administration (FDA)
Uses of AI in different sectors of drug discovery. The green boxes depict the domains immunitoAI is involved in.
Lead molecule discovery and optimisation: AI can make informed decisions about selecting and designing candidate leads for both small molecule and biologic drugs.
High-throughput screening: ML algorithms can predict the biological activity and likelihood of a drug binding to target proteins. This helps with virtually screening large libraries of small molecules to find hits. Therefore, reducing the number of compounds to be experimentally tested, saving time and resources.
Novel small molecule discovery: AI can learn patterns and relationships within molecular structures from massive datasets. This enables generation of novel compounds with the potential to become small molecule drugs. AI-driven approach significantly expands the exploration of chemical space, uncovering unique structures that might not be evident through traditional methods
Generics: Generics are small molecules, designed to be identical, or bioequivalent, to drugs that are already on the market. AI predicts how different components interact in generic drugs and ensures they are similar to the reference product.
Lead optimisation: ML-driven models predict how chemical modifications impact a small molecule's properties. This accelerates lead optimisation by suggesting modifications that enhance efficacy and safety.
Biosimilars: Biosimilars are biologics that are highly similar to an already approved reference biologic drug. AI compares biosimilars to reference biologics to ensure structural similarity and predict the functional properties of biosimilars.
Biobetters: Biobetters are altered versions of current biologic medications (including reference drugs and biosimilars). AI uses molecular and disease data to find targets for biobetters. ML-guided protein engineering helps in designing biosimilar and biobetters structures for enhanced efficacy and reduced side effects.
Novel biologics discovery and optimisation: AI uses predictive modelling and structural analysis to pinpoint areas of improvement in biologic molecules. It then proposes modifications while preserving the molecule's desired function and therapeutic properties. This approach helps design novel, more effective biologics.
Repurposing existing drugs: ML is used to analyse databases to identify approved drugs that could be repurposed for new indications, saving time and costs compared to developing entirely new drugs, in both small molecules and biologics.
Drug formulation: AI-assisted algorithms help refine formulations that can improve solubility, stability and bioavailability.
Antibodies bind specifically to antigens and are used as drugs for targeted therapy.
Novel Antibody Discovery
Antibody therapy, also called immunotherapy or biologic therapy, involves using natural or laboratory-made antibodies to target and treat diseases. Because of their high specificity, antibodies are on their way to becoming the largest component of the biologics landscape.
However, the time and money required in the conventional process of antibody discovery renders its potential to revolutionise healthcare untapped. The classical method of discovering antibodies is injecting an animal with the chosen target antigen to harvest immune cells that produce antibodies. This process is not only lengthy and laborious, but also suffers from a low success rate. To close the gap in the vertical of the rapidly growing market of novel antibody design, many companies are using AI-driven approaches. This method has made discoveries faster and efficient, while increasing the likelihood of success.
A common strategy is to predict how mutations or modifications to an existing antibody might impact their function and properties using ML models. AI suggests changes that enhance its attributes while preserving its therapeutic function. Most antibody companies today are using AI to optimise biologically derived or synthetically generated antibody sequences against known target antigens. However, these approaches do not come without caveats.
After synthetic design and AI-predicted optimisation, there is an immense load on experimental validation- a resource-intensive phase. This is where the functional and therapeutic properties of the designed molecule are tested.
Often, these synthetic molecules need to be significantly modified to achieve desired properties, for instance, a good degree of binding to the antigen and minimal toxicity.
Sometimes, the molecule loses its therapeutic properties in this process and cannot be developed into a viable drug.
And lastly, there is still a heavy reliance on biological sources for discovering novel antibodies.
To eliminate the reliance on animals and biological sources and increase efficiency, the solution is to use the power of AI to generate novel antibody drugs from scratch, designed to bind the target molecule specifically. This will increase the efficiency of the drug discovery and development process, thereby reducing both time and cost.
immunitoAI’s proprietary AI-platform is working towards this goal. For a deeper dive into immunitoAI's role in antibody discovery, check out our related blog.