{"id":1505,"date":"2022-09-14T21:26:20","date_gmt":"2022-09-14T21:26:20","guid":{"rendered":"https:\/\/www.idtdna.com\/page\/what-s-hot-in-synthetic-biology-drug-discovery"},"modified":"2025-08-22T16:21:31","modified_gmt":"2025-08-22T16:21:31","slug":"what-s-hot-in-synthetic-biology-drug-discovery","status":"publish","type":"post","link":"https:\/\/www.idtdna.com\/page\/support-and-education\/decoded-plus\/what-s-hot-in-synthetic-biology-drug-discovery\/","title":{"rendered":"What\u2019s hot in synthetic biology drug discovery?"},"content":{"rendered":"\n<p>For years, <a href=\"\/pages\/applications\/synthetic-biology\" rel=\"noreferrer noopener\">synthetic biology<\/a>&nbsp;aided our efforts to treat a myriad of aliments and illnesses. Now, with significant scientific advancements, the potential for new drug discovery has never looked more promising. When searching for the latest synthetic biology revelations, the number of available stories seem endless&mdash;in the best way possible. To help, we&rsquo;ve rounded up a few of the many noteworthy topics shaking up synthetic biology drug discovery conversations here at IDT.&nbsp;<\/p>\n<h2>Artificial intelligence and machine learning combine to accelerate antibody discovery&nbsp;<\/h2>\n<p>Artificial intelligence (AI) technologies&nbsp;now provide&nbsp;an antibody&nbsp;design platform that combines a high-speed wet lab with machine learning (ML) to create improved molecules through learnings from each experimental cycle.&nbsp;New&nbsp;AI\/ML models&nbsp;help&nbsp;determine how mutations affect an antibody&rsquo;s molecular properties to better inform scientists on high-quality options that might have otherwise remained hidden. This new technology shows promise for significantly accelerating new antibody discovery. For more information, check out our&nbsp;<a href=\"\/pages\/applications\/antibody-discovery\">antibody discovery<\/a>&nbsp;tools.&nbsp;<\/p>\n<h3>George Church and Nabla Bio receive funding to expand AI-designed antibodies&nbsp;<\/h3>\n<p>The father of synthetic biology is at it again, working with his team at <a href=\"https:\/\/www.fiercebiotech.com\/medtech\/ai-driven-antibody-designer-nabla-bio-raises-11m-seed-funding\" target=\"_blank\" rel=\"noreferrer noopener\">Nabla Bio<\/a>&nbsp;to create new antibody drugs that have been specifically engineered to address challenging clinical targets and to become commercially viable. Their process combines AI technology and wet-lab experimentation capable of analyzing billions of antibody sequences to build a &ldquo;biophysical fingerprint&rdquo; for one million new antibodies at a time, rapidly predicting the most effective options. This new approach has the scientific community on the edge of their seats with hopes to ramp up and improve the quality of antibodies moving from lab to clinic.&nbsp;&nbsp;<\/p>\n<h2>SARS-CoV-2 pandemic enables researchers to pinpoint effectiveness of semisynthetic na&iuml;ve antibody libraries&nbsp;<\/h2>\n<p>Earlier this year, <em>Nature Communications<\/em> published a <a href=\"https:\/\/www.nature.com\/articles\/s41467-021-27799-z\" target=\"_blank\" rel=\"noreferrer noopener\">study<\/a>&nbsp;on the ways the SARS-CoV-2 pandemic created a surge in comparative research that highlighted the benefits of semisynthetic na&iuml;ve antibody libraries. Historically, it was believed the most potent neutralizing antibodies had to be generated from convalescent patients and immunized animals. In this study, the team demonstrated how appropriately designed and constructed na&iuml;ve antibody libraries can effectively provide therapeutic antibodies against viral pathogens, potentially saving critical time and resources during future viral outbreaks.&nbsp;<\/p>\n<h2>LabGenius machine learning technology is revolutionizing drug discovery&nbsp;<\/h2>\n<p>The new platform technology, Eva&trade;, from our friends over at <a href=\"https:\/\/labgeni.us\/\" target=\"_blank\" rel=\"noreferrer noopener\">LabGenius<\/a>, rapidly pinpoints novel therapeutic antibodies for devastating illnesses like cancer and inflammatory diseases. This is all possible through machine learning models that can describe sequence-to-function relationships to accuracy evaluate protein fitness and improve multiple drug properties simultaneously. These ML approaches are trained to rapidly extrapolate ever-growing data libraries to make critical predications with heighted multi-parametric optimization and higher dimension perception that will dramatically reduce the historical drug discovery timeline and do so with greater accuracy.&nbsp;<\/p>\n<h2>Low-cost Mycobacterium tuberculosis (Mtb) drugs derived from engineered E. coli&nbsp;&nbsp;<\/h2>\n<p>Historical screening for <em>Mtb<\/em> has been complicated by the pathogen&rsquo;s slow growth period and biocontainment requirements. With the expansion of chemical genetics within synthetic biology, there is growing interest around new tools that will help to break through remaining challenges on the path toward new antimicrobial drug discovery.&nbsp;&nbsp;<\/p>\n<p>Recently, a <a href=\"https:\/\/www.nature.com\/articles\/s41467-022-31570-3\" target=\"_blank\" rel=\"noreferrer noopener\">study<\/a> sought to apply a synthetic biology framework for assaying <em>Mtb<\/em> drug targets in engineered <em>E. coli<\/em>. To do this, the research team built Target Essential Surrogate <em>E. coli<\/em> (TESEC) strains within which an essential <em>E. coli<\/em> enzyme was deleted and replaced with an equivalent target enzyme. They then screened 1,280 approved drugs and found that benazepril was an effective, expression-dependent inhibitor. Benazepril is currently used to control hypertension, but the completion of this study serves as a new proof of concept for future research to look at the 100 known conditionally essential <em>E. coli<\/em> metabolic genes for additional antibiotic discovery efforts.&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>For years, synthetic biology&nbsp;aided our efforts to treat a myriad of aliments and illnesses. Now, with significant scientific advancements, the potential for new drug discovery has never looked more promising. When searching for the latest synthetic biology revelations, the number of available stories seem endless&mdash;in the best way possible. To help, we&rsquo;ve rounded up a [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1506,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"ct_builder_shortcodes":"","ct_template_type":"","ct_parent_template":0,"inline_featured_image":false,"footnotes":""},"class_list":["post-1505","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Drug discovery research - The Latest News | IDT<\/title>\n<meta name=\"description\" content=\"Read how new studies, machine learning, and artificial intelligence combine to reshape drug discovery as we\u2019ve known it.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.idtdna.com\/page\/support-and-education\/decoded-plus\/what-s-hot-in-synthetic-biology-drug-discovery\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Drug discovery research - 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