In modern medicine, patient resistance has always been a problem that plagues drug developers. We urgently need to develop new drug screening methods. Creative Biolabs uses chemical genetics to expand the chemical space probed to discover new bioactive molecules, revive orphaned compounds, repurpose drugs and leverage off-target effects. In these cases, our approach can provide customers with a system-level view of the complex role, elucidating its role in targeting pathways, the role of drugs in pathways for access to cells, and revealing potential off-target effects.
With the development of modern biology, high-throughput reverse genetics methods represented by chemical genetics have been widely applied in the field of drug development. These high-throughput screening methods allow the analysis of whole-genome libraries containing mutants of each gene in the chromosome to understand the effects of the drug on the organism. Through extensive data collection, we can infer drug targets by comparing drug signatures.
In the process of cancer drug candidate localization, drug signature comparisons can provide information on the global effects of transcriptional perturbations of known targets and unknown off-targets, as well as information on how drugs work, and reveal potential off-target effects. This systematic view of the complex effect has a strong ability to describe and predict drug behavior at the level of MoA levels and drug interactions. Besides, linking chemical genetic data obtained by drug signature comparison with some single-cell parameters will allow the analysis of drugs that are not directed at cell growth and more broadly improve their resolution in dissecting the cellular effects of drugs.
Fig.1 Basic concepts and approaches in chemical genetics.
Creative Biolabs evaluated the compiled quantitative fitness for each of the mutants in the whole genome deletion library (all non-essential genes) in the presence of the drug by comparing drug signatures. Drugs with similar characteristics may share cellular targets and/or cytotoxic mechanisms. This association becomes more apparent when more drugs are analyzed, as duplicate "chemical genome" features reflecting the general drug Mode-of-Action (MoA) can be identified. Yet, drug signatures are driven by pathways controlling the intracellular drug concentration as much as they depend on pathways related to drug MoA or its cytotoxic effects to the cell. Thus, our machine learning algorithms can be used to identify chemical-genetic interactions that reflect the drug MoA. Also, we use the multi-parameter analysis of microscope images to screen the chemical genetics in cell lines, improve the resolution of MoA recognition, and help customers improve their ability to recognize MoA.
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