Lantern Pharma announces plans to enhance oncology drug discovery
It says it will focus on additional approaches to develop antibody drug conjugates (ADC), highly specific cancer-targeted antibodies linked to potential potent anti-tumor small molecules designed for the treatment of cancer.
Panna Sharma, Lantern’s CEO, and president, said: “RADR is an integral component for de-risking and powering the progression of Lantern’s drug programs, and our recent advances in moving from program identification through preclinical development have occurred at speeds rarely seen in oncology drug discovery and development.
“Globally, ADC drug programs are one of the fastest growing drug development markets and are projected to represent a global market potential of over $14 billion by 2027. The expansion of RADR’s ADC capabilities will not only build on its demonstrated ability to identify synergistic and effective combinations of antibodies and small molecules but will also facilitate new high-value ADC-focused business development opportunities and collaborations.”
Lantern says its strategic roadmap for the development of ADCs was implemented this quarter and will include developments of additional algorithms that can boost prediction of optimal combinations of ADC components including antibodies, antibody linkers, payloads, and ADC combinations with other anticancer small molecules.
The company says it will generate additional machine learning based ADC biomarker signatures that can predict a cancer’s sensitivity to and ADC and guide future patient selection for clinical trials.
Resistance from existing ADC payloads will, Lantern says, be overcome by using RADR guided selection of new molecule payloads with features of synergy or properties.
It will create AI modules to predict the immunogenicity of ADC antibodies to cancer cell surface antigens and expand its RADR’s existing billions of oncology-focused data points with the addition of immuno-oncology datasets.
The AI strategy, Lantern says, will enable the large-scale analysis of thousands of high-performing model features through their SHapley Additive exPlanation (SHAP) scores and can efficiently identify key genes and pathways that are mechanistically important to drug resistance, quality of patient outcomes, and improved delivery of ADC drug payloads.
These features can add potential value to ADC programs and prioritize ADC targets. Additionally, this powerful strategy can be leveraged to inform downstream ADC design by identifying ADC components that, when used together, have a high probability of synergy that can lead to therapeutic response.
