A review.Artificial intelligence can be achieved in many ways, the most common perhaps being machine learning.In this approach, a computer 'learns' to recognize patterns among scores of data, so that when a new data point is presented, the computer knows which pattern best fits the new information.Other methods of AI include neural networks-a type of machine learning wherein artificial networks are designed to resemble the way neurons in a human brain are connected, so as to simulate thinking.AI differs from conventional computer modeling in what information it requires to achieve a final result.With standard modeling, biologists need to know the relationship among the different data sets that are being mapped, according to Shah.Trying to track how mosquito populations affect the prevalence of malaria, for instance, requires knowing that there is a relationship between mosquitoes and malaria.But complex reactions such as drug response depend on several factors, including age, weight, genetics, proteins, disease type and many others that biologists might not even realize are important for predicting drug response.So, teasing out the exact relationships among all of the factors to determine how a drug will work becomes much more complicated.By contrast, AI doesn't require prior knowledge of the relationships between these different biol. factors."You throw up your hands and say, 'I'm not even going to try,'" Shah says.The human mind may be able to use conventional statistical modeling to map out tens of variables, but a computer that uses artificial intelligence can sift through millions of variables over an ever-faster timeframe.The agnostic approach to variables offered by AI has enabled researchers such as the Hos to quickly and accurately make predictions about drug responses.Chih-Ming Ho applied concepts from his days as an aerospace engineer, when he researched turbulence during flights, to simplify the process of determining drug response.One such concept-that of a complex system, where many different parts make it difficult to model-states that even if there are disturbances to the system, the way the system responds to any given disturbance is distillable into a fundamental equation that can be reliably modeled.The human body is one such complex system, Chih-Ming Ho recalls thinking nearly a decade ago.Given this, the response to a disturbance to the human body like a transplant surgery or swallowing a pill should be able to be mapped using a simple equation, he says.By this time, Ho had made the switch from studying turbulence to studying microfluidics, and then another to focus on deriving predictable reactions to drugs.He conducted laboratory experiments in which six different drugs in ten different dosages were applied in various combinations to human cell lines infected with herpes.The results-how many infected cells each drug killed while also preserving healthy cells-were fed to a computer after each experiment so that it could learn to rank the drugs and understand the pattern behind the ranking.After each experiment, the computer would eliminate possibilities of drug-dose combinations until, after a dozen or so iterations, the most successful cocktail emerged.The results were mapped onto a smooth curve in which the most elevated point represented the best option; the farther away the result was from the peak, the less optimum the drug-dose combination.After repeating the process with cells exposed to other pathogens such as the bacterium that causes tuberculosis as well as cancer cells, another pattern emerged.No matter the disease, the drugs or the cell lines, the smooth curve could always be represented by a simple algebraic equation, as Ho had predicted, and it always took between 10 and 20 iterations to arrive at the result.The team needed only to change out values in the equation that were specific to each patient and calculated based on the dosages that each patient had received.The result would be a personalized map of drug responses."I was really shocked," Chih-Ming Ho says."I couldn't believe that it could be so simple.".Since then, Ho and his son have tested this program, called Parabolic Personalized Dosing (PPD), in more than 30 different disease settings, and in more than 60 patients.These include the liver-transplant recipients in the 2015 trial, individuals with HIV infection and participants in an ongoing trial who have hematol. cancers.The Hos are currently in the process of getting their HIV trial results published.On their work predicting drug response so far, Dean Ho says, "We have zero misses.".He adds that their PPD program has thus far always managed to map out the best possible drug and dose combinations.Whereas the UCLA group is focused on tailoring treatment regimens of approved drugs, a group at Virginia Polytechnic University (Virginia Tech), led by nutritional immunologist Josep Bassaganya-Riera, is using elements of machine learning in the hope of predicting people's response to exptl. drugs.In a paper published in May, Bassanganya-Riera and his colleagues described how their combined modeling algorithm enabled them to identify the clin. responses that they might expect to see in patients with infections caused by a spore-forming type of bacteria called Clostridium difficile.Current treatment for C. difficile includes antibiotics, which wipe out even beneficial bacteria-and patients are often averse to other treatments, such as fecal transplants.Previous work in mice demonstrated that a prototype anti-inflammatory mol. called NSC61610 binds to a gut enzyme known as lanthionine synthetase C-like 2 (LANCL2) to dampen the inflammation caused by C. difficile, but also preserves the beneficial bacteria in the gut.However, the researchers were unsure whether this newly identified mol. would be an improvement on currently available therapies.In their latest study, Bassaganya-Riera and colleagues identified the cellular and mechanistic characteristics that make up clin. responses to current treatments for C. difficile infection; these include changes in the T cell population and the composition of the microbiome.The authors then tested NSC61610 in mice to chronicle the physiol. attributes that determine the response to this exptl. drug in the animals.The combination algorithm matched the characteristics determined from this mouse experiment to the human equivalent in patients treated with currently available drugs to predict how a patient might respond to the new drug.The study found, for instance, that NSC61610 outperformed antibiotics and antitoxin antibodies when it came to preserving commensal bacteria, which signified that the mol. merited further testing-this time, in patients."My high-level view is that this type of modeling allows us to simulate experiments that would take several months or years in a matter of three or four days," says Bassaganya-Riera, who is director of the Nutritional Immunol. and Mol. Medicine Laboratory at the Biocomplexity Institute of Virginia Tech and senior author of the study.But without machine learning, he says, the team would have been forced to select only two or three characteristics to compare to determine drug response.Machine learning allowed them to include up to 52 parameters."Every person is going to have their own certain set of parameters, and we need to understand what that unique mix of characteristics means, rather than analyzing each individual trait.Machine learning helps us to do that," he says.Bassaganya-Riera's company, Landos Biopharma, plans to initiate a phase 1 clin. trial in the second half of 2018 to test its candidate LANCL2-activating drug, BT-11, in individuals with Crohn's disease.
