Developing effective and quick-to-market medicines is a global priority. Off-target drug toxicities are a major roadblock to getting urgently needed drugs to market, contributing to the high attrition rates during both preclinical and clinical development. Now more than ever, as the world battles the second wave of the largest pandemic of recent times, quick and efficacious medicines remain of utmost importance.Cyclica, a global biotech company headquartered in Toronto, Canada, takes a polypharmacology approach to drug discovery by considering first and foremost all potential target interactions of a drug molecule. Powered by two machine learning engines, MatchMaker and POEM, Cyclica’s integrated artificial intelligence (AI)-augmented drug discovery platform enables multi-objective evaluation and design of drug candidates with favorable polypharmacological profiles and medicinal properties, while significantly reducing attrition rates and timelines to the clinic. Matchmaker and POEM. ADMET, absorption, distribution, metabolism and excretion toxicity; POEM, pareto-optimal embedded modelling.
‘We are unique in that we are taking a more holistic, yet personalized approach, to drug discovery by not looking only at one protein target or one well-characterized protein, but looking at the entire proteome and evaluating the polypharmacology of a given molecule,’ said Cyclica’s co-founder and CEO Naheed Kurji.Approaches for computer-aided drug design typically focus either on structure-based biophysics, which physically simulate how a molecule interacts with a specific protein at a given binding site, or on knowledge-based approaches that predict a molecule’s activity from aggregated biological and chemical data. However, both approaches work best on molecule classes and protein targets for which a wealth of data exists; their ability to extrapolate findings to novel targets and chemistries is limited.By leveraging both AI and computational biophysics, Cyclica’s platform facilitates the design of molecules that target less well characterized proteins, while also shedding light on the molecules’ mechanisms of action. Applications of Cyclica’s platform span target deconvolution to drug repurposing and de novo design—with the former being particularly important during a global pandemic to ensure therapies reach patients as soon as possible. Leveraging the strengths of its platform, Cyclica has launched over 20 collaborative research initiatives focused on identifying therapeutic options for the prevention and treatment of COVID-19 related illness.Matchmaker and POEMMatchmaker combines molecular biophysics and deep learning to predict binding of potential drug molecules across the human proteome with high speed and accuracy. ‘In our validation studies we demonstrated that Matchmaker is faster and has better predictive power than molecular docking approaches for binding prediction,’ said Kurji.Pareto-optimal embedded modeling (POEM) is a parameter-free supervised learning approach that predicts the medicinal properties of a molecule, offering insights into absorption, distribution, metabolism and excretion toxicity (ADMET) pharmacological properties and how they can be optimized. ‘When compared with other publicly available models for ADMET property prediction, POEM has come out consistently ahead,’ Kurji said. ‘At Cyclica we consider the downstream medicinal and developmental properties of a molecule in the ligand design process, unlike other companies that think of them as an afterthought,’ he added.Over the past 18 months, Cyclica has designed molecules for a wide range of diseases—including cancer, neurodegenerative and infectious diseases—that are progressing through preclinical development. ‘We have de novo designed molecules that have been synthesized, shown multitargeted biophysical activity, and can reduce tumor size in xenograft models,’ said Kurji. ‘Showing prospective validation like this is critically important; we can’t just say our cutting-edge approaches will work, we have to show it.’Strategic partnering modelTo further catalyze productivity in early stage R&D with its platform, Cyclica is keen to create companies and build its internal portfolio through partnerships. ‘Our partnership model is focused on spinning out companies with academic institutions, taking equity positions in early stage biotech companies, forming joint ventures or, in some cases, sharing the asset ownership of the molecule,’ Kurji explained.The company’s goal to kick off 20 drug discovery programs across many therapeutic areas in 2020 has so far been surpassed, with partnerships spanning 100 programs. In addition, Cyclica has just embarked on its largest joint venture with the University of Toronto to develop drugs against ‘undruggable’ cancer targets. ‘At Cyclica, we believe that to drive sustainable progress in reducing attrition rates and timelines to the clinic, an avant-garde approach to drug discovery, both scientifically and commercially, is required,’ said Kurji.
https://www.nature.com/articles/d43747-020-01141-w
