Innovations In AI and Digital Health<\/p>\n<\/aside>\n
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Because finding new, successful drugs has become so much harder, the average cost of bringing one to market nearly doubled between 2003 and 2013 to $2.6 billion, according to the Tufts Center for the Study of Drug Development. These same challenges have increased the lab-to-market time line to 12 years, with 90 percent of drugs washing out in one of the phases of human trials.<\/p>\n
It\u2019s no wonder, then, that the industry is enthusiastic about artificial-intelligence tools for drug development. These tools do not work by having expert-developed analytical techniques programmed into them; rather users feed them sample problems (a molecule) and solutions (how the molecule ultimately behaves as a drug) so that the software can develop its own computational approaches for producing those same solutions.<\/p>\n
Most AI-based drug-discovery applications take the form of a technique called machine learning, including a subset of the approach called deep learning. Most machine-learning programs can work with small data sets that are organized and labeled, whereas deep-learning programs can work with raw, unstructured data and require much larger volumes. Thus, a machine-learning program might learn to recognize the different features of a cell after being shown tens of thousands of examples of photographs of cells in which the parts are already labeled. A deep-learning version can figure out those parts on its own from unlabeled cell images, but it might need to look at a million of them to do it.<\/p>\n
Many scientists in the field think that AI will ultimately improve drug development in several ways: by identifying more promising drug candidates; by raising the \u201chit rate,\u201d or the percentage of candidates that make it through clinical trials and gain regulatory approval; and by speeding up the overall process. A machine-learning program recently deployed by Bristol-Myers Squibb, for instance, was trained to find patterns in data that correlate with CYP450 inhibition. Saha says the program boosted the accuracy of its CYP450 predictions to 95 percent\u2014a sixfold reduction in the failure rate compared with conventional methods. These results help researchers quickly screen out potentially toxic drugs and focus instead on candidates that have a stronger shot at making it all the way through multiple human trials to U.S. Food and Drug Administration approval. \u201cWhere AI can make a huge difference is having drugs that fail early on, before we make all that investment in them,\u201d says Vipin Gopal, chief data and analytics officer at Eli Lilly.<\/p>\n
Resources are now piling into the field. AI-based drug-discovery start-ups raised more than $1 billion in funding in 2018, and as of last September, they were on track to raise $1.5 billion in 2019. Every one of the major pharmaceutical companies has announced a partnership with at least one such firm. Only a few AI-discovered drugs are actually in the human-testing pipeline, however, and none has begun phase 3 human trials, the gold-standard test for experimental drugs. Saha concedes that it will be several years before he can say for sure whether the company\u2019s hit rates will go up as a result of the AI prediction rate of CYP450 inhibition. For all the hype in the industry, it is far from certain that early results will translate to more and better drugs.<\/p>\n
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Sifting through Millions of Molecules<\/b><\/p>\n
Emerging AI programs are not exactly a revolutionary update in the drug industry, which has for some time been building sophisticated analytical solutions that aid with drug development. The rise of powerful statistical and biophysical modeling programs well over a decade ago as part of the growth of the field of bioinformatics\u2014the quest to use computational tools to derive biological insights from large amounts of data\u2014led to tools that can predict the properties of molecules. But these programs have been limited by scientists\u2019 incomplete understanding of how molecules interact: they cannot tell conventional software how to find insights in data when they do not know what elements of the data are most important and how they relate to one another. Imbued with the ability to derive their own insights into which data elements matter, newer AI programs can extract better predictions for a wider range of variables.<\/p>\n
AI tools tackle different aspects of drug discovery in several ways. Some AI companies, for example, are focusing on the problem of designing a drug that can safely and effectively work on a known target\u2014usually a specific, well-studied protein that is associated with a disease. The goal is typically to come up with a molecule that can chemically bind to the target protein and modify it so that it no longer contributes to the disease or its symptoms. Cyclica, a Canadian firm, puts its software to work on matching the biophysical structures and biochemical properties of millions of molecules to the structures and properties of some 150,000 proteins to uncover molecules likely to bind to target proteins, as well as those to avoid.<\/p>\n
But molecules that are good candidates as drugs still have to jump through other hoops. Those include making it through the gut into the bloodstream without being immediately broken down by the liver or metabolic processes; working in a particular organ such as the kidney without disrupting other organs; avoiding binding to and incapacitating any of the thousands of other proteins in the human body that are important to health; and breaking down and leaving the body before drug levels become potentially dangerous. Cyclica\u2019s AI software takes all those requirements into consideration. \u201cA molecule that can interact with a protein target can usually interact with upward of 300 proteins,\u201d Cyclica\u2019s CEO Naheed Kurji says. \u201cIf you\u2019re designing a molecule, it behooves you to consider the other 299 interactions that could have disastrous effects in humans.\u201d<\/p>\n
There is growing recognition among biomedical researchers that complex diseases such as cancer and Alzheimer\u2019s involve hundreds of proteins, and hitting just one of them is not likely to be disruptive enough. Cyclica is attempting to find individual compounds that can interact with dozens of target proteins yet avoid interacting with hundreds of other proteins, Kurji explains. Currently under development, he adds, is the incorporation of a wealth of anonymized global genetic data about variations in proteins, so that the software can specify which patients the candidate drugs would work best on. Kurji claims that together these features will eventually be able to shave five years off the typical seven-year-long time frame for bringing a candidate drug from initial identification to human trials.<\/p>\n
Merck and Bayer are among the big pharma companies that have announced partnerships with Cyclica. As is the case with most AI-pharma partnerships, the companies are not releasing much insight into exactly what AI-generated drug candidates may be coming out of the collaborations. But Cyclica has shared some details of its successes in identifying a key target protein linked to already fda-approved drugs for systemic scleroderma, an autoimmune disease of the skin and other organs, as well as one linked to the Ebola virus. Each drug is already fda-approved for the treatment of other disorders\u2014HIV and depression, respectively\u2014which means they both could be quickly \u201crepurposed\u201d for the new applications if the research continues to pan out.<\/p>\n
Sometimes researchers identify a target protein that might play a critical role in disease but find that\u2014as is true of about 90 percent of the proteins in the human body\u2014not much is known about its structure and properties. With little data to go on, most machine- and deep-learning programs will not be able to figure out how to \u201cdrug\u201d the protein\u2014that is, come up with compounds that will bind to it and meet the other criteria for safety and efficacy. A handful of AI companies are focusing on these kinds of \u201csmall data\u201d problems, including Exscientia, which uses its software to hunt down molecules that might work with a target protein. It can produce useful insights with as few as 10 pieces of data about a protein, says the company\u2019s CEO, Andrew Hopkins, a professor of medicinal informatics at the University of Dundee in Scotland.<\/p>\n
Exscientia\u2019s algorithms compare the limited information available about a target protein against a database of about a billion protein interactions. This step narrows down the list of possible compounds that might work and specifies what additional data would help further refine the focus. Such data might come from looking at tissue samples to learn more about how the protein behaves in the body, for example. The resulting new data are then fed into the software, which pares the list again and suggests another round of needed data. This process is repeated until the software is ready to generate a manageable list of compounds that are favorable drug candidates for the target.<\/p>\n
Hopkins claims that Exscientia\u2019s process can cut the time spent in discovery from 4.5 years to as little as one year, reduces discovery costs by 80 percent and results in one-fifth the number of synthesized compounds as is normally needed to produce a single winning drug. Exscientia is partnering with biotech giant Celgene in an effort to find new potential drugs for three targets.<\/p>\n
Meanwhile an Exscientia partnership with GlaxoSmithKline has led to what the companies say is a promising molecule targeting a novel pathway to treat chronic obstructive pulmonary disease. But as with any AI company addressing drug development, Exscientia simply has not been in the game long enough to have generated enough new candidates that could have made it through to late-stage trials\u2014a process that typically takes five to eight years. Hopkins claims one of the candidates Exscientia has identified may reach human trials as early as this year. \u201cAt the end of the day we\u2019ll be judged on the drugs we deliver,\u201d he says.<\/p>\n
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The Need for New Targets<\/b><\/p>\n
Finding a molecule to hit a new target is not the only major challenge in drug discovery. There is also the need to identify targets in the first place. To spot proteins that might have roles in diseases, biopharma company Berg applies AI to sift through information derived from human tissue samples. This approach aims to solve two problems that hang over most research into drug targets, according to Berg\u2019s CEO Niven R. Narain: the efforts tend to be based on a researcher\u2019s theory or hunch, which can bias the results and overly restrict the pool of candidates, and they often turn up targets that are correlated to the disease but do not ultimately prove causative, which means drugging them will not help.<\/p>\n
Berg\u2019s approach involves plugging in every piece of data that can be wrung out of a patient\u2019s tissue samples, organ fluids and bloodwork. These extracted data include genomics, proteomics, metabolomics, lipidomics, and more\u2014an unusually broad range to consider in a hunt for targets. Samples are taken from people with and without a particular disease and at different stages of disease progression. Living cells from the samples are exposed in the laboratory to various compounds and conditions, such as low levels of oxygen or high levels of glucose. This method produces data on corresponding changes ranging from a cell\u2019s ability to produce energy to the rigidity of its membrane.<\/p>\n
All the data are then run through a set of deep-learning programs that search for any differences between nondisease and disease states, with an eye to eventually focusing on proteins whose presence seem to have an impact on the disease. In some cases, those proteins become candidates as targets, at which point Berg\u2019s software can start searching for compounds to drug those targets. What is more, because the software can discern when the target seems to cause disease in only a subset of patients, it can look for distinguishing characteristics of those patients, such as certain genes.That paves the way for a precision-medicine approach, meaning patients can be tested before they take the drug to determine whether it is likely to be effective for them.<\/p>\n
The most exciting drug to come out of Berg\u2019s work\u2014and perhaps the most exciting to emerge from any drug-discovery-related AI effort to date\u2014is a cancer drug called BPM31510. It recently completed a phase 2 trial for patients with advanced pancreatic cancer, which is extremely aggressive and difficult to treat. Phase 1 trials often do not indicate much about a drug\u2019s potential except whether it is dangerously toxic at a given dose, but BPM31510\u2019s phase 1 trial against other cancers provided some verification of the ability of Berg\u2019s software to predict the roughly 20 percent of patients who were likely to respond to it, as well as those who were more likely to experience adverse reactions.<\/p>\n
Additionally, tissue-sample analysis from the trial led Berg\u2019s software to predict, counterintuitively, that the drug would work best against more aggressive cancers because it attacks mechanisms that play a larger role in those cancers. Should the drug gain approval, Berg might do a postmarket analysis of perhaps one out of 100 patients taking it, \u201cso that we can keep improving how it\u2019s used,\u201d Narain says.<\/p>\n
Berg is partnering with pharma giant AstraZeneca to seek targets for Parkinson\u2019s and other neurological diseases and with Sanofi Pasteur to pursue improved flu vaccines. It is also working with the U.S. Department of Veterans Affairs and the Cleveland Clinic on targets for prostate cancer. The software has already identified mechanisms for diagnostic tests that could differentiate prostate cancer from benignly enlarged prostates, which currently is often difficult to do without surgery.<\/p>\n
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