MTHFD2

Drug discovery services partnership with Sino-American Cancer Foundation focuses on the development of actionable hits against the MTHFD2 target LANCASTER, Calif.--(BUSINESS WIRE)-- Simulations Plus, Inc. (Nasdaq: SLP), a leading provider of modeling and simulation software and services for pharmaceutical safety and efficacy, today announced that it established a strategic research collaboration with the Sino-American Cancer Foundation (SACF). This collaboration will leverage Simulations Plus’ staff and Artificial Intelligence-driven Drug Design (AIDD) technology in the ADMET Predictor® software platform to support the discovery and design of novel inhibitors of methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), an emerging cancer target. Per the terms of the collaboration, Simulations Plus will develop quantitative structure-activity relationship (QSAR) models for efficacy against MTHFD2, using information from SACF as well as academic and patent literature. The biologists, computational and medicinal chemists in the Early Drug Discovery Services team at Simulations Plus will work with the researchers at SACF to define the multi-objective parameters against which the lead molecule(s) will be optimized. The new AIDD Module in ADMET Predictor® will then be employed to generate libraries of virtual compounds that are optimized for potency and other chosen parameters. The teams will select promising candidates for synthesis and testing, and ensuing rounds of QSAR model building and AIDD optimization will be performed until the milestone criteria in the collaboration agreement are achieved. “We value the trust and confidence SACF has in our team and AI technology to complement and accelerate conventional drug design and lead optimization processes,” said Dr. Jeremy Jones, Principal Scientist at Simulations Plus and project lead. “By combining their drug discovery expertise with our algorithmic and data science know-how, we are confident we will successfully support their hit-to-lead target development activities.” As part of this agreement, SACF will provide upfront funding to Simulations Plus to design a set number of compounds for efficacy against MTHFD2 which will be exclusive to SACF. Subsequent milestone payments will be made as key research and development goals are met. “SACF has had an active interest in AI for de novo design for some time, and we have found a trusted partner in Simulations Plus to help us integrate this into our research,” added Dr. Frank Luh, CEO of SACF. “Scientists from the two organizations will work side-by-side to combine SACF’s data with all that Simulations Plus offers to identify the next generation of compounds that could help in the treatment of cancer.” John DiBella, SLP Division President, said, “The Early Drug Discovery Services offering at Simulations Plus is tailor made for this type of partnership, where our team of experts, including computational, medicinal, and cheminformatics specialists, provide end-to-end AI-driven drug discovery and optimization support to complement the SACF team. Simulations Plus continues to invite future collaborations which benefit organizations and, most importantly, the global communities we serve.” About Simulations Plus, Inc. Serving clients worldwide for more than 25 years, Simulations Plus is a leading provider in the biosimulation market providing software and consulting services supporting drug discovery, development, research, and regulatory submissions. We offer solutions that bridge machine learning, physiologically based pharmacokinetics, quantitative systems pharmacology/toxicology, and population PK/PD modeling approaches. Our technology is licensed and applied by major pharmaceutical, biotechnology, and regulatory agencies worldwide. For more information, visit our website at . Follow us on LinkedIn | Twitter | YouTube. About the Sino-American Cancer Foundation Established in 1995, the Sino-American Cancer Foundation is a non-profit organization dedicated to advancing cancer research, innovation, and technology. We aim to provide accurate, meaningful cancer information to help patients, physicians, and scientists make informed decisions about cancer prevention and treatment. SACF also offers a research facility to innovators, who are committed to applying rigorous scientific principles to advance the fields of science and medicine. Together, we seek to be recognized as a global leader in cancer research and drug development. Safe Harbor Statement Under the Private Securities Litigation Reform Act of 1995 – With the exception of historical information, the matters discussed in this press release are forward-looking statements that involve a number of risks and uncertainties. Words like “believe,” “expect” and “anticipate” mean that these are our best estimates as of this writing, but that there can be no assurances that expected or anticipated results or events will actually take place, so our actual future results could differ significantly from those statements. Factors that could cause or contribute to such differences include, but are not limited to: our ability to maintain our competitive advantages, acceptance of new software and improved versions of our existing software by our customers, the general economics of the pharmaceutical industry, our ability to finance growth, our ability to continue to attract and retain highly qualified technical staff, our ability to identify and close acquisitions on terms favorable to the Company, and a sustainable market. Further information on our risk factors is contained in our quarterly and annual reports and filed with the U.S. Securities and Exchange Commission.

In a study published Sept. 21 in Nature Metabolism, researchers described how they used a machine learning approach called collateral lethal genes identification via metabolic fluxes, or CLIM, to identify backup gene pairs and study cancer cell metabolism. Cancer cells will stop at nothing to grow—right down to deleting sections of their DNA that contain growth suppression genes. In the process, they sometimes delete genes they need to regulate crucial metabolic pathways, requiring them to recruit backup genes, or paralog genes, to make up for the missing functionality. Pinning down paralog genes generally requires tedious screenings that give limited information about the metabolic pathways they're regulating. Now, a team of researchers from the University of Michigan and Indiana University has used machine learning to streamline the process and add context that could make precision medicines more precise. “This could revolutionize the precision medicine field because the drug targeting will only affect and kill cancer cells and spare the normal cells,” Deepak Nagrath, Ph.D., senior author of the study, said in a press release. “Most cancer drugs affect normal tissues and cells. However, our strategy allows specific targeting of cancer cells.” In their study, published Sept. 21 in Nature Metabolism, the researchers described how they used a machine learning approach called collateral lethal genes identification via metabolic fluxes, or CLIM, to identify paralog gene pairs and study cancer cell metabolism. This information is key to developing new, more effective therapies, first author Abhinav Achreja, Ph.D., explained in an interview. “Our platform actually makes predictions based on the modeling of the metabolism,” Achreja said. “When it's making predictions about whether different genes interact, it’s already answering the question to why the link between them exists in the first place.” The researchers' lab has long studied metabolic processes in ovarian cancer. While doing some data mining to find a good case study for their algorithm, they noticed that one gene in ovarian cancer cells, UQCR11, was often deleted alongside a growth suppressor gene. UQCR11 plays an important role in cell respiration; if the cells were still surviving, it meant they must be making up for its function elsewhere.  With the help of CLIM, the scientists pinned down the cells' Plan B. Deleting UQCR11 upregulated the activity of a gene called MTHFD2, which was supplying the necessary components for cell respiration in its absence. A series of cell culture experiments and animal testing backed up the algorithm's results. Inhibiting MTHFD2 in cells without UQCR11 had profound effects on cell metabolism. Transplanting ovarian cancer cells in which MTHFD2 was knocked down into mice where UQCR11 was also deleted led to cancer remission.   Notably, the cancer cells the researchers studied all had distinctly different genetic profiles and tumor microenvironments—two ways by which they could make up for the loss of the primary and backup genes. The fact that inhibiting MTHFD2 in cells without UQCR11 resulted in cancer remission suggests that it has broad efficacy, the researchers noted in their paper. This isn’t the first time MTHFD2 has been implicated in ovarian cancer or other types of cancer, for that matter. It’s highly expressed in a variety of malignancies—both solid and hematological—and researchers have proposed multiple mechanisms for its role. But while there has been no shortage of promising preclinical research on MTHDF2 inhibitors, treatments targeting it have not yet made it to clinical trials. This is where CLIM can help, Achreja said. The algorithm showed that MTHFD2 plays a very specific role in filling in for UQCR11—one that might have been hidden from other, less robust analysis. Perhaps it’s in a subset of patients whose cancer cells have MTHFD2 working as a backup gene for UQCR11 that its inhibition would have the most therapeutic benefit.   “By finding this link, we may be able to improve MTHDF2 gene treatments by at least making screening patients better, where we’re looking into their tumors and seeing that MTHDF2 is playing this role,” Achreja said. “This could let us pick patients out as viable candidates for MTHDF2-targeting treatments.”

The way that tumor cells enable their uncontrolled growth is also a weakness that can be harnessed to treat cancer, researchers at the University of Michigan and Indiana University have shown. Their machine-learning algorithm can identify backup genes that only tumor cells are using so that drugs can target cancer precisely. The team demonstrated this new precision medicine approach treating ovarian cancer in mice. Moreover, the cellular behavior that exposes these vulnerabilities is common across most forms of cancer, meaning the algorithms could provide better treatment plans for a host of malignancies. "This could revolutionize the precision medicine field because the drug targeting will only affect and kill cancer cells and spare the normal cells," said Deepak Nagrath, a U-M associate professor of biomedical engineering and senior author of the study in Nature Metabolism. "Most cancer drugs affect normal tissues and cells. However, our strategy allows specific targeting of cancer cells." This approach is known as collateral lethality - using information gleaned from genes that cancer cells discard to find weaknesses. The human body comes with many mechanisms designed to protect against cancer. Cancer cells themselves used to contain suppressor genes that prevent their spread. Those cells however, have a clever strategy for dealing with that; they simply delete a portion of their DNA - the part that includes those suppressor genes. In doing so, the cells typically lose other genes that are necessary for survival. To avoid death, the cells find a paralog - a gene that can serve a similar function. Usually there are one or, possibly, two genes that can step in and perform the same function to keep the cell alive. What if you could identify the right paralog and target it in a way that shuts down its vital function for the cell? "When a direct replacement for the deleted metabolic gene is not available, our algorithms use a mathematical model of the cancer cells' metabolism to predict the paralogous metabolic pathway they might use," said Abhinav Achreja, a U-M research fellow in biomedical engineering and lead author on the research paper. "These metabolic pathways are important to the cancer cells and can be targeted selectively." Attacking metabolic pathways essentially shuts down the cell's energy source. In examining ovarian cancer cells, U-Ms team zeroed in on one gene, UQCR11, that was often deleted along with a suppressor gene. UQCR11 plays a vital role in cell respiration - how cells break down glucose for energy in order to survive. Disturbances in this process can lead to a major imbalance of an important metabolite, NAD+, in the mitochondria, where respiration takes place. Despite all odds, ovarian cancer cells continue to thrive by relying on their backup plan. U-M's algorithm correctly sorted through multiple options and successfully predicted a cell missing UQCR11 would turn to the gene MTHFD2 as its backup supplier of NAD+. Researchers at the Indiana University School of Medicine helped validate the findings in the lab. This team, led by professor of medicine Xiongbin Lu, developed genetically modified cell and animal models of ovarian cancers with the deletions. Six out of six mice tested showed complete cancer remission. This research was supported by funding from the National Cancer Institute, Office of the Director for the National Institutes of Health, University of Michigan Precision Health Scholars Award, and Forbes Scholar Award from Forbes Institute of Cancer Discovery. Achreja A, Yu T, Mittal A et al. Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer. Nat Metab 4, 1119-1137, 2022. doi: 10.1038/s42255-022-00636-3