[{"data":1,"prerenderedAt":422},["ShallowReactive",2],{"slug-top-companies-using-a-i-in-drug-discovery-and-development":3},{"post":4,"relatedPosts":187,"relatedBooks":332},{"id":5,"date":6,"date_gmt":7,"guid":8,"modified":10,"modified_gmt":11,"slug":12,"status":13,"type":14,"link":15,"title":16,"content":18,"excerpt":21,"author":23,"featured_media":24,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":29,"categories":30,"tags":34,"project_category":48,"contact_email_category":49,"yst_prominent_words":50,"class_list":61,"better_featured_image":84,"acf":116,"yoast_meta":132,"_links":134},24858,"2023-08-17T10:00:00","2023-08-17T08:00:00",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=24858&_wpnonce=7146ccf2fa&status=auto-draft&type=post","2023-08-17T09:36:42","2023-08-17T07:36:42","top-companies-using-a-i-in-drug-discovery-and-development","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002Ftop-companies-using-a-i-in-drug-discovery-and-development",{"rendered":17},"Top 6 Companies Using AI In Drug Discovery And Development",{"rendered":19,"protected":20},"\n\u003Cdiv class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n\u003Cdiv class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n\u003Cp>What if coming up with a new drug could be measured in days rather than years? What if new medication would cost thousands instead of billions of dollars? Just look at how an AI pharma start-up \u003Ca href=\"https:\u002F\u002Fwww.technologyreview.com\u002Ff\u002F614251\u002Fan-ai-system-identified-a-potential-new-drug-in-just-46-days\u002F\">developed a potential new drug in 46 days\u003C\u002Fa>! Artificial intelligence technologies \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-future-of-clinical-trials-artificial-patients-synthetic-data-and-real-time-analysis\" target=\"_blank\">promise to speed up the process\u003C\u002Fa> of drug discovery and development and make it more cost-effective. As the market is flourishing, and it takes time and effort to separate the wheat from the chaff, we collected the most promising AI pharma companies out there.\u003C\u002Fp>\n\n\n\n\u003Cp>Drug design is a key area AI is revolutionizing. In one of our latest database projects, we decided to venture into this territory and collect the companies that use artificial intelligence for this purpose. To add more value than what’s already available, our goal was not only to list these but also to determine their fields of operation, their target group and other, so far unmapped details.\u003C\u002Fp>\n\u003C\u002Fdiv>\n\u003C\u002Fdiv>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The future is about speed and savings\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>New drugs are approved through human clinical trials: rigorous,\nyear-long procedures starting in animal trials and gradually moving to\npatients. They typically cost billions of dollars and take many years to\ncomplete, sometimes more than a decade. Plus, patients in trials are exposed to\nside effects that cannot be predicted or expected. And even if the trial is\nsuccessful, it has to go through a regulatory approval: it may or may not\nreceive the nod of the respective regulatory agency, e.g. the US Food and Drugs\nAdministration (FDA).\u003C\u002Fp>\n\n\n\n\u003Cp>Luckily, there’s an abundance of technologies and companies that want to change the status quo. One way to \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffuture-of-clinical-trials\" target=\"_blank\">modernise the drug testing process\u003C\u002Fa> is applying technologies to the traditional framework, for example, through online platforms to seek out participants; while an alternative way is to use \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwyss.harvard.edu\u002Ftechnology\u002Fhuman-organs-on-chips\u002F\" target=\"_blank\">human organs-on-chips\u003C\u002Fa> or \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fin-silico-trials-are-the-future\u002F\" target=\"_blank\">in silico trials\u003C\u002Fa>. These all aim to speed up the process, save thousands of dollars and unburden patients. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>However, when looking at all these technologies, artificial intelligence is the most advanced and most multifaceted. The technology helps companies aggregate and synthesize a lot of information that’s needed for clinical trials, thus shortening the drug development process. It can also help understand the mechanisms of the disease, establish biomarkers, generate data, models, or novel drug candidates, design or redesign drugs, run preclinical experiments, design and run clinical trials, and even analyse the real-world experience\u003C\u002Fstrong>. \u003C\u002Fp>\n\n\n\n\u003Cp>The number of already existing AI companies in drug development reflects the manifold usage of the technology: they are many and increasing day by day. \u003C\u002Fp>\n\n\n\n\u003Cp>Nevertheless, we managed to choose our six favorites, so here you go!\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">1) The company that mines clinical trials: \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.antidote.me\u002F\" target=\"_blank\">Antidote\u003C\u002Fa>\u003C\u002Fh2>\n\n\n\n\u003Cp>US and UK-based Antidote is focusing on matching patients and medical researchers in clinical trials so they could work together more easily. \u003Cstrong>The platform allows patients to find the most suitable clinical trials, helps researchers stream their latest study information to millions of patients, and even connects them with members of the medical community directly\u003C\u002Fstrong>. It’s basically a very efficient online platform for enhancing access to clinical trials.\u003C\u002Fp>\n\n\n\n\u003Cp>By combining proprietary technologies, data, and well-established business models, the company is transforming the way patients and researchers connect so that breakthroughs can happen faster. The company was launched under the name of TrialReach in 2010, but it got rebranded to Antidote in 2016. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">2) The company with tangible results: \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.atomwise.com\u002F\" target=\"_blank\">Atomwise\u003C\u002Fa>\u003C\u002Fh2>\n\n\n\n\u003Cp>\u003Cstrong>The most well-known company in drug discovery aims to reduce the costs of medicine development by using supercomputers to predict from a database of molecular structures in advance which potential medicines will work, and which won’t\u003C\u002Fstrong>. Their deep convolutional neural network, AtomNet, screens more than 100 million compounds each day.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F09\u002Ftmf_article_291_A-02-768x432.png\" alt=\"\" class=\"wp-image-36305\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F09\u002Ftmf_article_291_A-02-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F09\u002Ftmf_article_291_A-02-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F09\u002Ftmf_article_291_A-02-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F09\u002Ftmf_article_291_A-02.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>In 2015, Atomwise launched a virtual search for safe, existing medicines that could be redesigned to treat the Ebola virus. They found two drug candidates predicted by the company’s AI technology which may significantly reduce Ebola infectivity. This analysis, which typically would have taken months or years, was completed in less than one day! Imagine how efficient drug creation would become if such clinical trials could be run at the “ground zero” level of health care, namely in pharmacies.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">3) The company that concentrates on cancer drugs: \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.welcome.ai\u002Fturbine-ai\" target=\"_blank\">Turbine.AI\u003C\u002Fa>\u003C\u002Fh2>\n\n\n\n\u003Cp>A dedicated team of A.I. developers, medical professionals, and bioinformaticians has spent 6 years researching and building an artificial intelligence solution to design personalised treatments for any cancer type or patient faster than any traditional healthcare service. \u003Cstrong>The technology models cell biology on the molecular level, it can identify the best drug to target a specific tumor with; moreover, it identifies complex biomarkers and design combination therapies by performing millions of simulated experiments each day.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\">\u003Cdiv class=\"wp-block-embed__wrapper\">\n\u003Ciframe loading=\"lazy\" title=\"AI Case Studies In Pharmaceutical Drug Design  - Live Q&A With The Medical Futurist\" width=\"640\" height=\"360\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FcSrwgtEAzLQ?start=1045&feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen>\u003C\u002Fiframe>\n\u003C\u002Fdiv>\u003C\u002Ffigure>\n\n\n\n\u003Cp>The key to Turbine’s uniqueness is its molecular model of cancer biology guided by an AI to identify the biomarkers that signal sensitivity to treatment. As a result, the technology is already used in collaborations with Bayer, the University of Cambridge and top Hungarian research groups to find new cancer cures, speed up the time to market them and save the lives of patients suffering from currently incurable forms of the lethal disease.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">4) The company with a comprehensive structure from genomic to clinical data\u003Cstrong>: \u003C\u002Fstrong>\u003Ca rel=\"noreferrer noopener\" href=\"http:\u002F\u002Fprecisionlife.com\u002F\" target=\"_blank\">Row Analytics\u003C\u002Fa>\u003C\u002Fh2>\n\n\n\n\u003Cp>\u003Cstrong>The Oxford-based data analytics company operating since 2013 specializes in digital health, precision medicine, genomics, and semantic search\u003C\u002Fstrong>. It is delivering a range of highly innovative data analytics platforms, for example, PrecisionLife for drug discovery.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>The platform combines AI methods and data analytics to look at multiple genetic variants in combinations across a range of diseases\u003C\u002Fstrong>. As they are able to complete the process in weeks instead of months, even for large disease populations with tens of thousands of patients, this enables the rapid identification of novel drug candidates and potential drugs to repurpose. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">5) The company with research excellence in in-silico genomics: \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.deepgenomics.com\u002F\" target=\"_blank\">Deep Genomics\u003C\u002Fa>\u003C\u002Fh2>\n\n\n\n\u003Cp>Brendan Frey’s company promises \u003Cstrong>to solve the biggest puzzle in genetics: to get to know exactly what information the genome could provide for patients, medical professionals, and researchers. For doing so, Deep Genomics is \u003Ca href=\"http:\u002F\u002Fwww.businessinsider.com\u002Fhow-deep-genomics-is-using-ai-to-solve-genetic-mysteries-2015-9\" target=\"_blank\" rel=\"noreferrer noopener\">leveraging AI, specifically deep learning to help decode the meaning of the genome\u003C\u002Fa>\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>So far, the company has used its computational system to develop a database that provides predictions for more than 300 million genetic variations that could affect the genetic code. For this reason, their findings are used for genome-based therapeutic development, molecular diagnostics, targeting biomarker discovery and assessing risks for genetic disorders.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">6) The company with drug discovery in 46 days: \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Finsilico.com\u002F\" target=\"_blank\">Insilico Medicine\u003C\u002Fa>\u003C\u002Fh2>\n\n\n\n\u003Cp>\u003Cstrong>As an artificial intelligence company for drug discovery, biomarker development, and ageing research, Insilico Medicine aims to cover the entire process of drug discovery, clinical trials analysis, and digital medicine\u003C\u002Fstrong>. It is pursuing internal drug discovery programs in cancer, dermatological diseases, fibrosis, Parkinson’s Disease, Alzheimer’s Disease, ALS, diabetes, sarcopenia, and ageing.\u003C\u002Fp>\n\n\n\n\u003Cp>The company, working with researchers at the University of Toronto, \u003Cstrong>made headlines lately with the announcement that the process of developing a new drug candidate lasted just 46 days with the help of its smart algorithm\u003C\u002Fstrong>. At first, it took \u003Ca href=\"https:\u002F\u002Fwww.technologyreview.com\u002Ff\u002F614251\u002Fan-ai-system-identified-a-potential-new-drug-in-just-46-days\u002F\">21 days for the team to create 30,000 designs for molecules\u003C\u002Fa> that target a protein linked with fibrosis (tissue scarring). They synthesized six of these molecules in the lab and then tested two in cells, the most promising one was tested in mice. The researchers concluded it was potent against the protein and showed “drug-like” qualities. The research was published in\u003Ca href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-019-0224-x\"> Nature Biotechnology\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Ctable style=\"width: 100%; border-collapse: collapse; background-color: #eee; border-top: 4px solid #444;\" cellpadding=\"10px\">\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd style=\"width: 100%;\">\n\u003Cp>\u003Cstrong>At The Medical Futurist, we are building a community for making a bold vision about the future of healthcare reality today.\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Ciframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FFIbMejImnxs\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen=\"\">\u003C\u002Fiframe>\n\u003Cp>If you’d like to support this mission, we invite you to \u003Ca href=\"https:\u002F\u002Fwww.patreon.com\u002Fthemedicalfuturist\">join The Medical Futurist Patreon Community\u003C\u002Fa>. A community of empowered patients, future-oriented healthcare professionals, concerned health policymakers, sensible health tech developers, and enthusiastic medical students. If there were ever a time to join us, it is now. Every contribution, however big or small, powers our research and sustains our future.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.patreon.com\u002Fthemedicalfuturist\">\u003Cstrong style=\"background-color: #e71d3299; color: #000;\">Click here to support The Medical Futurist from as little as $3\u003C\u002Fstrong>\u003C\u002Fa> – it only takes a minute. Thank you.\u003C\u002Fp>\n\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003Cp> \u003C\u002Fp>\n\n\n\n\u003Cp>\u003C\u002Fp>\n",false,{"rendered":22,"protected":20},"\u003Cp>What if coming up with a new drug could be measured in days rather than years? What if new medication would cost thousands instead of billions of dollars? Artificial intelligence technologies promise to speed up the process of drug discovery and make it more cost-effective. As the market is flourishing, and it takes time and effort to separate the wheat from the chaff, we collected the most promising AI pharma companies out there.\u003C\u002Fp>\n",6,52039,"closed",true,"","standard",{"_acf_changed":20,"footnotes":27},[31,32,33],504,490,488,[35,36,37,38,39,40,41,42,43,44,45,46,47],134,568,144,1154,173,1471,217,1472,262,1473,356,373,566,[],[],[51,52,53,54,55,56,57,58,59,60],1715,1739,1833,1883,2935,1599,1607,1621,1631,1635,[62,14,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83],"post-24858","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-future-of-pharma","category-genomics","tag-ai","tag-drug-development","tag-artificial-intelligence","tag-medication","tag-clinical","tag-drug-discovery","tag-drug","tag-drug-research","tag-genomics","tag-cure","tag-pharma-2","tag-research","tag-clinical-trials",{"id":24,"alt_text":27,"caption":27,"description":27,"media_type":85,"media_details":86,"post":114,"source_url":115},"image",{"width":87,"height":88,"file":89,"filesize":90,"sizes":91,"image_meta":111},1500,844,"2023\u002F08\u002Ftmf_article_382-2-s.png",598938,{"medium":92,"large":99,"thumbnail":105,"medium_large":110},{"file":93,"width":94,"height":95,"mime-type":96,"filesize":97,"source_url":98},"tmf_article_382-2-s-370x208.png",370,208,"image\u002Fpng",56575,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F08\u002Ftmf_article_382-2-s-370x208.png",{"file":100,"width":101,"height":102,"mime-type":96,"filesize":103,"source_url":104},"tmf_article_382-2-s-768x432.png",768,432,168332,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F08\u002Ftmf_article_382-2-s-768x432.png",{"file":106,"width":107,"height":107,"mime-type":96,"filesize":108,"source_url":109},"tmf_article_382-2-s-150x150.png",150,20749,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F08\u002Ftmf_article_382-2-s-150x150.png",{"file":100,"width":101,"height":102,"mime-type":96,"filesize":103,"source_url":104},{"aperture":112,"credit":27,"camera":27,"caption":27,"created_timestamp":112,"copyright":27,"focal_length":112,"iso":112,"shutter_speed":112,"title":27,"orientation":112,"keywords":113},"0",[],null,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F08\u002Ftmf_article_382-2-s.png",{"related_posts_footer":117,"related_posts":20,"cta_type":121,"cta_color":122,"subtitle":27,"related_books":123,"key_takeaways":127},[118,119,120],14131,15585,46538,"subscribe","green",[124,125,126],24764,24762,24761,[128,130],{"title":129},"\u003Cp>AI promises to disrupt drug discovery, radically speeding up the process and making it significantly cheaper\u003C\u002Fp>\n",{"title":131},"\u003Cp>We collected the flagship companies of the field and also introduce our latest database project analysing this realm\u003C\u002Fp>\n",{"yoast_wpseo_title":17,"yoast_wpseo_metadesc":133,"yoast_wpseo_canonical":15},"AI promises to speed up and make drug discovery and development more cost-effective. Here, we collected the most promising AI pharma companies.",{"self":135,"collection":141,"about":144,"author":147,"replies":150,"version-history":153,"predecessor-version":157,"wp:featuredmedia":161,"wp:attachment":164,"wp:term":167,"curies":183},[136],{"href":137,"targetHints":138},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F24858",{"allow":139},[140],"GET",[142],{"href":143},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts",[145],{"href":146},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fpost",[148],{"embeddable":26,"href":149},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F6",[151],{"embeddable":26,"href":152},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=24858",[154],{"count":155,"href":156},29,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F24858\u002Frevisions",[158],{"id":159,"href":160},52065,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F24858\u002Frevisions\u002F52065",[162],{"embeddable":26,"href":163},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F52039",[165],{"href":166},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=24858",[168,171,174,177,180],{"taxonomy":169,"embeddable":26,"href":170},"category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=24858",{"taxonomy":172,"embeddable":26,"href":173},"post_tag","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=24858",{"taxonomy":175,"embeddable":26,"href":176},"project_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=24858",{"taxonomy":178,"embeddable":26,"href":179},"contact_email_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=24858",{"taxonomy":181,"embeddable":26,"href":182},"yst_prominent_words","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=24858",[184],{"name":185,"href":186,"templated":26},"wp","https:\u002F\u002Fapi.w.org\u002F{rel}",[188],{"id":120,"date":189,"date_gmt":190,"guid":191,"modified":193,"modified_gmt":194,"slug":195,"status":13,"type":14,"link":196,"title":197,"content":199,"excerpt":201,"author":23,"featured_media":203,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":204,"categories":205,"tags":211,"project_category":221,"contact_email_category":223,"yst_prominent_words":224,"class_list":228,"better_featured_image":245,"acf":281,"yoast_meta":288,"_links":290},"2022-05-31T10:00:00","2022-05-31T08:00:00",{"rendered":192},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=46538&_wpnonce=d0b76defed&status=auto-draft&type=post","2022-10-17T14:09:07","2022-10-17T12:09:07","the-future-of-clinical-trials-artificial-patients-synthetic-data-and-real-time-analysis","https:\u002F\u002Fmedicalfuturist.com\u002Fthe-future-of-clinical-trials-artificial-patients-synthetic-data-and-real-time-analysis",{"rendered":198},"The Future Of Clinical Trials: Artificial Patients, Synthetic Data And Real-Time Analysis",{"rendered":200,"protected":20},"\n\u003Cp>In this article, we summarise three concepts that are already\u002F will soon become familiar for everyone interested in the future of clinical trials. These are 1. the concept of the artificial patient, 2. using synthetic data and 3. real-time analyses.\u003C\u002Fp>\n\n\n\n\u003Cp>What connects the three is that all are potential tools to make clinical trials faster, cheaper and safer. That is so, in an ideal world. Because, as almost always, there is a catch, not even a small one. But more on it later. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">The artificial patient\u003C\u002Fh2>\n\n\n\n\u003Cp>\u003Cstrong>What is it? \u003C\u002Fstrong>As of today, there is no final, widely accepted definition of what an artificial\u002Fvirtual\u002Fsynthetic patient is. A very detailed explanation about some (but not all) of the different definitions can be found \u003Ca href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC4318546\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">in this study\u003C\u002Fa>. \u003C\u002Fp>\n\n\n\n\u003Cp>So let me phrase it simply what we mean by it in this analysis: \u003C\u002Fp>\n\n\n\n\u003Cblockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\u003Cp>an artificial patient is a set of data representing the desired human characteristics the best possible way that is based on large amounts of real patient data, without actually including any backtracable real-patient data. \u003C\u002Fp>\u003C\u002Fblockquote>\n\n\n\n\u003Cp>\u003Cstrong>Why is it? \u003C\u002Fstrong>Artificial patients can be the answer to more than one problems of modern medicine. One of them is patient privacy. With ever more machine learning and deep learning models being used, A.I. needs huge amounts of data to learn from. But providing a lot of real patient data is against their privacy rights, and we have seen ample examples of how bad an idea it is to allow random companies to access heaps of sensitive health data. On the other hand, taking a real-life dataset of existing humans, and generating a synthetic dataset that resembles the original in all important aspects (*more on it later) without actually including anything personal can be a solution.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>What is it good for?\u003C\u002Fstrong> Artificial patients can be used for a number of things, from medical education to clinical trials, this time we are only focusing on the latter. One day, virtual patients might become the go-to tools for\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\u003Cli>estimating efficiency and potential side effects of \u003Ca href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC7577280\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">promising drug molecules\u003C\u002Fa> or \u003Ca href=\"https:\u002F\u002Fbiblio.ugent.be\u002Fpublication\u002F8711822\u002Ffile\u002F8711823.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">optimising the use of existing ones\u003C\u002Fa>, \u003C\u002Fli>\u003Cli>to model the success rate of \u003Ca href=\"https:\u002F\u002Fwww.mdpi.com\u002F2077-0375\u002F12\u002F6\u002F548\" target=\"_blank\" rel=\"noreferrer noopener\">future medical devices\u003C\u002Fa> or \u003Ca href=\"https:\u002F\u002Fwww.medgadget.com\u002F2021\u002F06\u002Fin-silico-clinical-trial-replicates-results-of-traditional-trial.html\" target=\"_blank\" rel=\"noreferrer noopener\">treatment methods\u003C\u002Fa>, \u003C\u002Fli>\u003Cli>or, as the latest,\u003Ca href=\"https:\u002F\u002Fwww.theverge.com\u002F2022\u002F4\u002F28\u002F23044586\u002Fvr-chronic-pain-synthetic-clinical-trial-data\" target=\"_blank\" rel=\"noreferrer noopener\"> they can substitute the placebo control group\u003C\u002Fa> for clinical trials\u003C\u002Fli>\u003C\u002Ful>\n\n\n\n\u003Cp>As many hope, one day artificial patients may be able to completely substitute humans and animals in clinical trials, most likely with animals being the first. \u003C\u002Fp>\n\n\n\n\u003Cp>While using artificial patients for drug development or medical device development is a promising field, there is a long way to go until the models can reach the required complexity while being truly representative of the human population.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large is-style-default\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F093_will_we_born_in_2050-768x432.png\" alt=\"artificial womb\" class=\"wp-image-24181\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F093_will_we_born_in_2050-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F093_will_we_born_in_2050-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F093_will_we_born_in_2050-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F093_will_we_born_in_2050-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F093_will_we_born_in_2050.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>On the other hand, artificial patients as the placebo control group have arrived. AppliedVR \u003Ca href=\"https:\u002F\u002Fwww.theverge.com\u002F2022\u002F4\u002F28\u002F23044586\u002Fvr-chronic-pain-synthetic-clinical-trial-data\" target=\"_blank\" rel=\"noreferrer noopener\">recently conducted a trial\u003C\u002Fa> for VR treatment for chronic back pain patients. And instead of recruiting patients to sign up for the trials to not receive the treatment (being the control group), they decided to turn to an existing database of chronic pain patients, provided by healthcare data company Komodo Health. \u003C\u002Fp>\n\n\n\n\u003Cp>Using real-world data as a patient group in a trial, often known as a synthetic control arm, can make research trials more efficient — companies don’t have to enrol as many people in clinical trials and can guarantee that those who apply will indeed receive the treatment. \u003C\u002Fp>\n\n\n\n\u003Cp>Synthetic control groups can also improve equity in clinical research. “That allows us to go look at all those different subpopulations and underrepresented patient populations to see if they have different outcomes,” Web Sun, president and co-founder of Komodo Health says. \u003C\u002Fp>\n\n\n\n\u003Cp>Another interesting example was this\u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.medgadget.com\u002F2021\u002F06\u002Fin-silico-clinical-trial-replicates-results-of-traditional-trial.html\" target=\"_blank\"> virtual trial\u003C\u002Fa> (or in-silico trial) carried out to predict the efficiency of using flow diverters for brain aneurysms. Researchers created 82 virtual patients based on data of real patients from previous flow diverter trials. The experiment projected an 82.9% success rate for the use of diverters, pretty close to the results of three real-world flow diverter trials that had 86.8%, 74.8% and 76.8% success rates respectively.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">Synthetic data\u003C\u002Fh2>\n\n\n\n\u003Cp>If you felt like your head started spinning from dealing with the concept of the artificial patient, behold, synthetic data is probably an even wilder ride. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>What is it?\u003C\u002Fstrong> The definition is simpler this time: synthetic data is the use of A.I. to create datasets that mimic the real world.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Why is it?\u003C\u002Fstrong> Because we 1. don’t have enough real-world data or 2. don’t want to use real-world (sensitive) data.\u003Cbr>\u003Cstrong>What is it good for?\u003C\u002Fstrong> Feeding any algorithm that needs massive amounts of data to learn and either develop new prediction capabilities or recognise patterns. Synthetic data is widely used in a number of industries and segments, not just in medicine, but also in self-driving vehicles, security, robotics, fraud protection, insurance models, military and so on.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large is-style-default\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health-768x432.png\" alt=\"health data\" class=\"wp-image-24937\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health-512x288.png 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>Artificial intelligence has earned its place \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fsynthetic-data-in-healthcare-will-smarter-data-bring-the-a-i-revolution-in-healthcare\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">in multiple fields of medicine\u003C\u002Fa>, from recognising patterns, supporting diagnoses and setting up treatment pathways to optimising healthcare logistics. Smart algorithms can sift through large volumes of data no man can, deriving clear-cut trends from such analyses. \u003C\u002Fp>\n\n\n\n\u003Cp>Privacy concerns limit the amount of available data in medicine. Working with sensitive patient data is a tricky issue. It seems we cannot keep our privacy intact AND also benefit from A.I.’s advantages in our care. We saw in many cases how sensitive information can get leaked \u003Ca href=\"https:\u002F\u002Fwww.securitymagazine.com\u002Farticles\u002F96399-over-800-million-medical-records-exposed-in-data-breach\" target=\"_blank\" rel=\"noreferrer noopener\">even unintentionally\u003C\u002Fa> – and we are not even talking about hacking or privacy, just a poorly protected database. New methods like \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffederated-learning-can-protect-patients-data-in-hospitals\">f\u003C\u002Fa>\u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffederated-learning-can-protect-patients-data-in-hospitals\" target=\"_blank\" rel=\"noreferrer noopener\">e\u003C\u002Fa>\u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffederated-learning-can-protect-patients-data-in-hospitals\">derated learning\u003C\u002Fa> might make it possible to do this without breaching patients’ privacy, but its scope is limited.\u003C\u002Fp>\n\n\n\n\u003Cp>That’s when synthetic data comes in. It can fill in the missing data, making it possible to produce entirely fabricated patient datasets that are just as useful for training A.I. as the real thing, while keeping patient data protected.\u003C\u002Fp>\n\n\n\n\u003Cp>Using synthetic data could help overcome this challenge as the training could focus on such variables, making use of real-world environments. Using the above-mentioned example, how to diagnose melanoma on dark skin toned patients – as often\u003Ca href=\"https:\u002F\u002Fwww.vice.com\u002Fen\u002Farticle\u002Fm7evmy\u002Fgoogles-new-dermatology-app-wasnt-designed-for-people-with-darker-skin\" target=\"_blank\" rel=\"noreferrer noopener\"> previous algorithms have failed\u003C\u002Fa> to be able to do so.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">But not everything is just sunshine and sandy beaches\u003C\u002Fh2>\n\n\n\n\u003Cp>This \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fiai.tv\u002Farticles\u002Fwe-should-all-be-worried-about-synthetic-data-auid-2138&utm_source=reddit&_auid=2020\" target=\"_blank\">insightful analysis\u003C\u002Fa> not only explains how and why synthetic data is used, but also why we should be scared of it. In short: any dataset we create will be imperfect to some extent. It will contain biases we are not aware of. It will not include important variables we either overlooked or are not aware of their importance. Even, in the best of cases, it will be like a snapshot of a given moment of a given situation. \u003C\u002Fp>\n\n\n\n\u003Cp>If we let machine learning and deep learning algorithms develop on these synthetic, imperfect datasets, chances are they will come to conclusions that are more or less false in the real world. \u003C\u002Fp>\n\n\n\n\u003Cp>And the reason why this is worrying is the speed at which the use of synthetic data is spreading. Gartner predicts that by 2024 some 60% of all data used for AI will be synthetic. And not just in medicine. \u003C\u002Fp>\n\n\n\n\u003Cp>Synthetic data is cheap and easy to come by, much easier and cheaper than collecting huge amounts of messy real-world data. What happens if decisions affecting large groups of people or whole societies will be made based on it? \u003C\u002Fp>\n\n\n\n\u003Cp>It is especially worrying as the world already faces challenges regarding “truth”. Introducing alternative truths based on ‘data’ to back decisions affecting societies – like healthcare funding, insurance models and so on – can have devastating consequences.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">Real-time \u002F decentralised clinical trials\u003C\u002Fh2>\n\n\n\n\u003Cp>\u003Cstrong>What is it?\u003C\u002Fstrong> The use of electronic health data\u002Frecords\u002Fdevices to carry out clinical trials in near real-time with patients not needed to be present on site.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Why is it? \u003C\u002Fstrong>Real-time trials offer faster results and the possibility of participants to directly connect to other patients, share their experiences and get access to results. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>What is it good for?\u003C\u002Fstrong> More committed participants and faster results. \u003C\u002Fp>\n\n\n\n\u003Cp>We have seen a few good examples in recent months.\u003C\u002Fp>\n\n\n\n\u003Cp>One is that of \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.mobihealthnews.com\u002Fnews\u002Froyal-philips-rolls-out-home-ecg-system-decentralized-clinical-trials\" target=\"_blank\">Royal-Philips rolling out a new at-home ECG system\u003C\u002Fa> for decentralised clinical trial use. The company is pitching this new technology as a way for clinical trial participants to record ECG data without travelling to a clinical site or requiring an in-home clinician. Data of trial participants can be transmitted near real-time to the cloud servers for analysis. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large is-style-default\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"494\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2016\u002F09\u002Fclinical-trials-768x494.jpg\" alt=\"Clinical Trials\" class=\"wp-image-14141\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2016\u002F09\u002Fclinical-trials-768x494.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2016\u002F09\u002Fclinical-trials-512x330.jpg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2016\u002F09\u002Fclinical-trials-389x250.jpg 389w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2016\u002F09\u002Fclinical-trials.jpg 870w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>A number of digital health companies are designing tools to enable decentralised trials. In September, digital wound-care company \u003Ca href=\"https:\u002F\u002Fwww.mobihealthnews.com\u002Fnews\u002Fswift-medicals-new-imaging-platform-expands-digital-wound-care-company-decentralized-trials\" target=\"_blank\" rel=\"noreferrer noopener\">Swift Medical\u003C\u002Fa> launched a new digital-imaging platform designed to support decentralised clinical trials. The technology was designed to aid in large-scale image collection and management in order for researchers to monitor the impact of medical interventions at various sites or at home.\u003C\u002Fp>\n\n\n\n\u003Cp>Deploying such advanced technologies in clinical trials will require pharmaceutical and biotech companies to commit not only financially but also to the idea that technologies can significantly contribute to clinical trials, making drugs cheaper, making the process faster and much more importantly, making the lives of patients participating in them more comfortable.\u003C\u002Fp>\n",{"rendered":202,"protected":20},"\u003Cp>Three important concepts explained that are expected to make future clinical trials faster, safer and cheaper, without real humans and animals.\u003C\u002Fp>\n",24545,{"_acf_changed":20,"footnotes":27},[206,207,31,208,209,210],7079,6261,798,491,521,[212,213,214,215,216,217,218,47,219,220],7477,7577,1530,7578,7579,306,7580,636,671,[222],953,[],[58,59,60,225,226,227],1693,1803,1807,[229,14,63,64,65,66,67,230,231,68,232,233,234,235,236,237,238,239,240,241,83,242,243,244],"post-46538","category-tmf","category-forecast","category-digital-health-research","category-empowered-patients","category-future-medicine","tag-synthetic-data","tag-artificial-patient","tag-drug-design","tag-virtual-patient","tag-synthetic-patient","tag-medical-device","tag-real-time-analysis","tag-deep-learning","tag-machine-learning","project_category-researchers",{"id":203,"alt_text":246,"caption":27,"description":247,"media_type":85,"media_details":248,"post":279,"source_url":280},"in silico trials, synthetic data, artificial patient, TMF","synthetic data, artificial patient, 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