Clinical stage artificial intelligence (AI) drug discovery company Insilico Medicine (“Insilico”) has been invited to present scientific data on its novel anti-cancer assets at three major upcoming cancer conferences — the European Society for Medical Oncology (ESMO) conference in Madrid Oct. 20-24, 2023; the Society of Immunotherapy of Cancer (SITC) conference Nov. 1-5, 2023 in San Diego; and the San Antonio Breast Cancer Symposium (SABCS) Dec. 5-9, 2023. 

Small molecule oncology target inhibitors represent the largest part of Insilico’s therapeutic pipeline portfolio, which includes 31 programs across 29 targets. The Company recently entered into a licensing deal with Exelixis on its potentially best-in-class generative AI-designed USP1 inhibitor for BRCA-mutant tumors for $80m upfront and additional milestone payments and tiered royalties. That drug is currently in a Phase I clinical trial. 

“Using our AI platform, we have been able to advance a number of anti-cancer therapeutics that use new mechanisms to stop tumor growth and cancer progression, including two in clinical stage,” says Sujata Rao, MD, Chief Medical Officer at Insilico Medicine. Dr. Rao has extensive experience in clinical oncology practice and over 15 years in pharma leading global clinical development for cancer drugs. “Driven by a strategy of focusing on novelty, confidence, and commercial tractability, and designed to meet the high unmet medical needs of patients, we have developed a number of promising anti-cancer assets and look forward to presenting to the leading cancer conferences.”

Dr. Rao showcased four of the Company’s novel AI-designed cancer inhibitors at the most recent Association for Cancer Research (AACR) annual meeting.

Insilico’s upcoming cancer conference presentations include:

• ESMO – Oct. 20-24: At ESMO, Insilico will present data on ISM8207, a novel QPCTL inhibitor for triple negative breast cancer and B-cell non-Hodgkin lymphoma. The small molecule inhibitor, currently being evaluated in a Phase I trial, has demonstrated anti-tumor activity in both hematological tumors and solid tumors in preclinical studies, as well as favorable pharmacokinetics and safety profiles. This molecule is available for licensing partners in the U.S., Europe and Japan. 
• SITC – Nov. 1-5: At SITC, Insilico will present data on ISM5939, a novel, potent, orally available, selective ENPP1 inhibitor cancer immunotherapy for multiple tumor types that enhances the anti-tumor effects of immune checkpoint inhibitors in syngeneic murine cancer models. It is also being advanced as a possible treatment for Hypophosphatasia.
• SABCS – Dec. 5-9: At SABCS, Insilico will present data on ISM5043, a novel, selective KAT6 inhibitor for the treatment of advanced ER+/HER2- breast cancer – the most common subtype of breast cancer. Current treatment for patients with advanced or metastatic disease is endocrine therapy in combination with CDK4/6 inhibitors but many patients develop resistance to therapy, indicating a huge unmet medical need. 

Insilico is advancing new therapeutics using generative AI via its proprietary end-to-end Pharma.AI platform for identifying novel targets (PandaOmics), designing new drugs (Chemistry42), and predicting the outcomes of clinical trials (InClinico). The platform has produced four drugs that have reached clinical trials, including a lead drug for the devastating chronic lung disease Idiopathic Pulmonary Fibrosis (IPF), the first AI-discovered and AI-designed drug to advance to Phase II trials with patients. 

“We’re really encouraged by the progress of our diverse pipeline of cancer therapeutics – two of which have progressed into clinical trials,” says Alex Zhavoronkov, PhD, founder and CEO of Insilico Medicine. “Our AI can be thought of as a ‘Google for targets’ that looks at every single small signal from massive datasets all over the world, including our own robotics data. It gives us signals that the target is working in a specific cancer, it already has demonstrated some efficacy, and it is going to be commercially tractable.” 

Dr. Rao and Insilico’s Chief Business Officer Michelle Chen, PhD, along with other business development professionals, will be in attendance at the upcoming conferences. For any interest in licensing or partnerships, please contact: bd@insilicomedicine.com.

About Insilico Medicine

Insilico Medicine, a global clinical stage biotechnology company powered by generative AI, is connecting biology, chemistry, and clinical trials analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques for novel target discovery and the generation of novel molecular structures with desired properties. Insilico Medicine is developing breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system diseases, infectious diseases, autoimmune diseases, and aging-related diseases. www.insilico.com

A new kind of polymer membrane created by researchers at Georgia Tech could reshape how refineries process crude oil, dramatically reducing the energy and water required while extracting even more useful materials.

The so-called DUCKY polymers — more on the unusual name in a minute — are reported Oct. 16 in Nature Materials. And they’re just the beginning for the team of Georgia Tech chemists, chemical engineers, and materials scientists. They also have created artificial intelligence tools to predict the performance of these kinds of polymer membranes, which could accelerate development of new ones.

The implications are stark: the initial separation of crude oil components is responsible for roughly 1% of energy used across the globe. What’s more, the membrane separation technology the researchers are developing could have several uses, from biofuels and biodegradable plastics to pulp and paper products.

“We’re establishing concepts here that we can then use with different molecules or polymers, but we apply them to crude oil because that’s the most challenging target right now,” said M.G. Finn, professor and James A. Carlos Family Chair in the School of Chemistry and Biochemistry.

Crude oil in its raw state includes thousands of compounds that have to be processed and refined to produce useful materials — gas and other fuels, as well as plastics, textiles, food additives, medical products, and more. Squeezing out the valuable stuff involves dozens of steps, but it starts with distillation, a water- and energy-intensive process.

Researchers have been trying to develop membranes to do that work instead, filtering out the desirable molecules and skipping all the boiling and cooling.

“Crude oil is an enormously important feedstock for almost all aspects of life, and most people don’t think about how it’s processed,” said Ryan Lively, Thomas C. DeLoach Jr. Professor in the School of Chemical and Biomolecular Engineering. “These distillation systems are massive water consumers, and the membranes simply are not. They’re not using heat or combustion. They just use electricity. You could ostensibly run it off of a wind turbine, if you wanted. It’s just a fundamentally different way of doing a separation.”

What makes the team’s new membrane formula so powerful is a new family of polymers. The researchers used building blocks called spirocyclic monomers that assemble together in chains with lots of 90-degree turns, forming a kinky material that doesn’t compress easily and forms pores that selectively bind and permit desirable molecules to pass through. The polymers are not rigid, which means they’re easier to make in large quantities. They also have a well-controlled flexibility or mobility that allows pores of the right filtering structure to come and go over time.

The DUCKY polymers are created through a chemical reaction that’s easy to produce at a scale that would be useful for industrial purposes. It’s a flavor of a Nobel Prize-winning family of reactions called click chemistry, and that’s what gives the polymers their name. The reaction is called copper-catalyzed azide-alkyne cycloaddition — abbreviated CuAAC and pronounced “quack.” Thus: DUCKY polymers.

In isolation, the three key characteristics of the polymer membranes aren’t new; it’s their unique combination that makes them a novelty and effective, Finn said.

The research team included scientists at ExxonMobil, who discovered just how effective the membranes could be. The company’s scientists took the crudest of the crude oil components — the sludge left at the bottom after the distillation process — and pushed it through one of the membranes. The process extracted even more valuable materials.

“That’s actually the business case for a lot of the people who process crude oils. They want to know what they can do that’s new. Can a membrane make something new that the distillation column can’t?” Lively said. “Of course, our secret motivation is to reduce energy, carbon, and water footprints, but if we can help them make new products at the same time, that’s a win-win.”

Predicting such outcomes is one way the team’s AI models can come into play. In a related study recently published in Nature Communications, Lively, Finn, and researchers in Rampi Ramprasad’s Georgia Tech lab described using machine learning algorithms and mass transport simulations to predict the performance of polymer membranes in complex separations.

“This entire pipeline, I think, is a significant development. And it’s also the first step toward actual materials design,” said Ramprasad, professor and Michael E. Tennenbaum Family Chair in the School of Materials Science and Engineering. “We call this a ‘forward problem,’ meaning you have a material and a mixture that goes in — what comes out? That’s a prediction problem. What we want to do eventually is to design new polymers that achieve a certain target permeation performance.”

Complex mixtures like crude oil might have hundreds or thousands of components, so accurately describing each compound in mathematical terms, how it interacts with the membrane, and extrapolating the outcome is “non-trivial,” as Ramprasad put it.

Training the algorithms also involved combing through all the experimental literature on solvent diffusion through polymers to build an enormous dataset. But, like the potential of membranes themselves to reshape refining, knowing ahead of time how a proposed polymer membrane might work would accelerate a materias design process that’s basically trial-and-error now, Ramprasad said.

“The default approach is to make the material and test it, and that takes time. This data-driven or machine learning-based approach uses past knowledge in a very efficient manner,” he said. “It’s a digital partner: You’re not guaranteed an exact prediction, because the model is limited by the space spanned by the data you use to train it. But it can extrapolate a little bit and it can take you in new directions, potentially. You can do an initial screening by searching through vast chemical spaces and make go, no-go decisions up front.”

Lively said he’d long been a skeptic about the ability of machine learning tools to tackle the kinds of complex separations he works with.

“I always said, ‘I don’t think you can predict the complexity of transport through polymer membranes. The systems are too big; the physics are too complicated. Can’t do it.’”

But then he met Ramprasad: “Rather than just be a naysayer, Rampi and I took a stab at it with a couple of undergrads, built this big database, and dang. Actually, you can do it,” Lively said.

Developing the AI tools also involved comparing the algorithms’ predictions to actual results, including with the DUCKY polymer membranes. The experiments showed the AI models predictions were within 6% to 7% of actual measurements.

“It’s astonishing,” Finn said. “My career has been spent trying to predict what molecules are going to do. The machine learning approach, and Rampi’s execution of it, is just completely revolutionary.”

This research was supported by the U.S. Department of Energy, grant No. DE-EE0007888; the European Research Council, grant No. 758370; the Kwanjeong Educational Foundation; a Royal Society University Research Fellowship; and the ExxonMobil Technology and Engineering Company. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any funding agency.