What's the fuss behind polypharmacology & multi-targeted drug design?

Originally posted on LinkedIn: https://bit.ly/3bpyKpG

It is well understood that while the common “one drug, one target” paradigm has contributed to the discovery of novel medicines, it has many limitations today. As I wrote about in an article published in Forbes in Aug 2019, single-target drug design is not adequate for complex diseases, where multiple pathogenic factors modulate molecular pathways in the body. As Professor Ross Cagan from the Icahn School of Medicine and Mount Sinai University stated in a seminal 2012 paper, “the complexity of cancer has led to recent interest in polypharmacological approaches for developing kinase-inhibitor drugs…” Given that approximately 300 protein targets are predicted to interact with a single drug, single-target drugs that simply account for one target, and do not consider off-target interactions, are posing a significant risk in drug discovery and in patients (Zhou et al. 2015). An understanding of a drug’s polypharmacology is necessary to not only develop effective medicines but also mitigate downstream costs associated with off-target failures that may arise in drug development.

Image Credit to Dr. Vijay Shahani, Vice President of Drug Discovery, Cyclica

In addition, there are many important criteria to advance a drug through preclinical testing and into clinical trials including understanding, and optimizing for, ADMET-properties (pharmacokinetics - PK) of the drug candidate. At Cyclica, our scientific vision is to design drugs for patients not just for a protein target. As Professor Andreas Bender from the University of Cambridge recently stated: “We don’t develop drugs against proteins, we intend to treat people – hence, how about the pharmacokinetics of the compound, efficacy and safety?“ 

At Cyclica, our flagship technology, Ligand Express was developed to augment the capabilities of scientists, and leverage artificial intelligence (AI) and computational biophysics to ascertain the polypharmacological profile and ADMET-properties of drug candidates (or drugs). This holistic perspective has provided our partners with a deeper understanding of how a drug interacts with all the proteins in the body. Recently, we launched Ligand Design, a drug design platform that generates novel lead-like molecules optimized against multiple targets and multi-objective parameters for desired physicochemical, pharmacokinetic, and polypharmacological characteristics.

The platform enables scientists to select a panel of targets (both desirable targets and undesirable anti-targets), provide seed molecules, specify physiochemical and ADMET properties, and use an evolutionary algorithm to generate a collection of novel multi-targeted molecules. The generated molecules are drug-like in nature and synthetically accessible making it practical for medicinal chemists to synthesize. Ligand Design can also screen a catalogue of pre-existing molecules that are readily available for purchase should synthesis be infeasible or undesirable for our partner.

Figure 2: Ligand design digitizes the design-make-test-analyze cycle to generate advanced lead molecules with preferred physiochemical, pharmacokinetic and polypharmacological properties. 

The benefits of Ligand Design are not only in its predictive power but also in its speed. Ligand Design can generate molecules that explore a chemical space of 10^60, assess 4 million novel chemical entities, and filter these compounds by chosen properties, all within approximately 20 hours of computing time. By providing scientists with lead-like compounds at this tremendous speed, Ligand Design can accelerate decision-making in pharma, uncover overlooked possibilities, and demonstrate validated accuracy.  

While multi-target drug discovery has not been the dominant approach for decades, the time is truly now. In a recent article published by Nature Medicine, When there’s more than one way to target a cancer, author Mike May quotes Heather Carlson, an expert in bioinformatics and structure-based drug design at the University of Michigan College of Pharmacy, who states, “This is a great time to tackle polypharmacology, specifically because informatics tools are readily available to the public.” The article also describes how Ligand Design is being used by scientists to search for new cancer drugs, and the results have shown that the drug candidates have good solubility, safety, efficacy, and pharmacokinetics properties within in vivo models. Moreover, users of the platform are amazed by the promising results.  

Figure 3: Ligand Design and Ligand Express provide an integrated, holistic, and end-to-end enabling platform focused on polypharmacology

Taken together, Ligand Design and Ligand Express offer a unique end-to-end enabling AI-augmented drug discovery platform to design advanced lead-like molecules that minimize unwanted off-target effects, while providing a holistic understanding of a molecule's activity through integrated systems biology and structural pharmacogenomics. Both work together to design molecules that modulate desired targets and avoid harmful ones and screen these compounds against the human proteome to determine their polypharmacological profile. With an integrated platform, Cyclica opens new opportunities for drug discovery, including multi-targeted and multi-objective drug design, lead optimization, target deconvolution, and drug repurposing. This applies to a broad range of small molecule drug classes, including PROTACs and novel scaffolds.

There are a variety of ways scientists, even without computational expertise, can engage with the platform. Scientists can use Ligand Express to screen molecules from phenotypic screens to identify novel targets for the mechanism of action purposes, or use Ligand Design to optimize molecules that require adjustments. They may also use Ligand Express to deconvolute the mechanism of observed toxicity by finding toxic targets, then leverage Ligand Design to discover novel lead-like molecules and avoid undesirable targets. Cyclica’s platform provides scientists with an opportunity to augment their efforts at multiple stages in the drug discovery process, seamlessly integrating into workflows. With Ligand Design’s flexibility, scientists can engineer multi-targeted molecules to act as anchors for several target proteins, pre-evaluated with linkers or with ubiquitin recruiters for next-generation PROTACs. Alternatively, scientists can design molecular probes or tool compounds to assess biology or act as enhancers for more complex therapeutic modalities like gene editing with CRISPR-Cas9. 

Our goal at Cyclica is to decrease the time-consuming and costly process from 7 years to 2 by empowering domain expert scientists to make better and faster decisions with an integrated platform. By doing so, we can help them take more steps in the right direction and fewer in the wrong direction. We firmly believe that it is necessary for computer scientists, data engineers, experimental scientists, academics, and key system stakeholders to meaningfully collaborate to develop medicines that target the root of disease, and address the challenges that exist in the pharmaceutical and healthcare industries today. We also believe that the future of drug discovery is in the hands of early-stage and hyper innovative biotech companies. By collaborating with these companies, we can spark a new wave of innovation and create the pipeline of the future that will then be carried through the clinic and onto the market by large pharma companies. By doing more with AI and leveraging multidisciplinary work, the partnership between scientists and technologists cannot be understated. It is through these partnerships that we can design better medicines, and ultimately improve the lives of patients around the world.