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SAN DIEGO—Early this year, in Molecular Cell, two papers by University of California, San Diego researchers described new technologies that enable a detailed analysis of how genetic mutations and chemical compounds affect the molecular machinery inside a cell. This, in turn, the university researchers note, “could lead to entirely new ways to predict a drug’s activity in the human body, ultimately leading to new treatment approaches or unexpected new uses of existing FDA-approved drugs.”
The two papers are titled “Systematic Gene-to-Phenotype Arrays: A High-Throughput Technique for Molecular Phenotyping” and “The Dfm1 Derlin Is Required for ERAD Retrotranslocation of Integral Membrane Proteins.”
The studies in those papers introduce a technology that enables the rapid, large-scale screening of complex cell responses (or phenotypes) independent of growth. The researchers demonstrated the power of this Systematic Gene-to-Phenotype Array (SGPA) technology and a new yeast mutant library by investigating protein folding, degradation and DNA transcription.
Reportedly, this work uncovered new biological insights that were previously missed in fitness-only experiments and, according to Dr. Philipp Jaeger, a postdoctoral researcher and first author of one of the studies, this opens the door to a much more detailed assessment of drugs in this new form of chemogenetic screening, promising to deliver much higher molecular resolution on what exactly a compound does in a cell.
However, testing drugs for fitness effects across mutants, integrating dozens or even hundreds of defined molecular readouts and following the associated effects over time, generates what the researchers call “a four-dimensional dataset.” The complications of this means that the latest generation of computational analysis—machine learning—is needed to make sense of all the data. To this end, Jaeger founded BiocipherX Inc., a start-up company that is currently developing this new drug discovery and prediction approach.
A better heart model?
SAN DIEGO—In early February, in what is being touted as first-of-its-kind preclinical ex-vivo human cardiac research, AnaBios and Amgen published work in the journal Frontiers in Physiology reportedly demonstrating that an isolated human heart-based model, combined with integrated analysis using a newly developed pro-arrhythmic score, can differentiate between pro-arrhythmic and non-pro-arrhythmic compounds and has a greater predictivity when compared to models derived from human stem cells.
As the companies note, cardiac safety remains the leading cause of drug development discontinuation, and strategies to improve cardiac safety at the preclinical stage have thus far shown limited predictivity—thus, a quest has ensued in the industry to develop better drug-induced cardiotoxicity assessment with human-relevant models.
“Our innovative translational approaches enable human-focused drug discovery and help ensure the safety and efficacy of new treatments,” said Dr. Andre Ghetti, CEO of AnaBios.
According to the company’s vice president of research and development, Dr. Najah Abi-Gerges: “Data from this research demonstrates that isolated adult human ventricular tissue enables the generation of reliable and predictive data for human-focused cardiac safety assessment at early stages in drug discovery, provides a good opportunity to prioritize compounds and eliminates the potential for cross-species differences.”
For this research, human ventricular trabeculae isolated from ethically consented organ donors were used to assess the effects of 13 blinded clinical reference drugs on cardiac action potential. When the pro-arrhythmia risk was evaluated at tenfold of the effective therapeutic plasma concentration of each drug, the pro-arrhythmia score had high sensitivity and specificity. This high predictivity supports the translational potential of the new model, the partner companies say, and indicates it could become a reliable component of preclinical risk assessment.
The importance of native kinase profiling
LA JOLLA, Calif.—ActivX Biosciences Inc., a wholly owned subsidiary of Tokyo-based Kyorin Pharmaceutical Co. Ltd., announced in mid-January two reports published in peer-reviewed journals by Prof. Nathanael Gray at the Dana Farber Cancer Institute, Jay Bradner of Novartis and their colleagues, in which the KiNativ platform was used to generate critical data supporting these studies.
Selective protein kinase degradation using heterobifunctional molecules consisting of separate binding elements for a kinase active site and an E3 ubiquitin ligase has emerged as a promising new modality for drug development, according to ActivX. In contrast to the traditional approach of inhibiting the enzymatic activity of a kinase, once these bifunctional compounds bind to the targeted kinase, they recruit the ubiquitin-ligase which then can lead to quantitative loss of target enzyme through proteasome-dependent degradation. Several companies have been founded to develop such compounds, including Arvinas, C4 Therapeutics and Kymera.
In both publications, well-characterized kinase inhibitors were converted to degraders by conjugation with a thalidomide derivative that binds the Cereblon E3 ubiquitin ligase. In one report, published in Cell Chemical Biology, a promiscuous kinase inhibitor was converted to a degrader. Quantitative proteomics was used to identify 28 out of about 300 kinases that underwent degradation. The initial results were then used to guide the development of selective degraders for FLT3 and BTK. In the second report, published in Nature Chemical Biology, a CDK binding element was converted into a degrader, yielding a CDK9 selective degrader.
In both studies, the KiNativ platform was used to monitor the kinases that bound the degraders by measuring in-cell target engagement. Importantly, the number of kinases that bound the degraders was significantly higher than the number of kinases that underwent degradation, indicating that there is not a straightforward correlation between the affinity of a degrader for a kinase and its ability to degrade the kinase.
Thus, the KiNativ platform reportedly provides a unique approach to monitoring the kinases capable of binding a degrader by interrogating those kinases that are in fact degraded, compared to those that inhibit the targeted kinase but are not slated for degradation.
“2018 will be the year we see an exponential increase in the number of small-molecule degraders targeting a host of different protein targets,” Gray predicted. “As the efficiency of degradation does not exclusively depend on typical parameters around target occupancy, proteomic technologies (both gene-family directed and global) that quantitatively measure protein abundance will be critical to evaluating the selectivity of new degrader molecules.”