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Scientists rewrites FDArecommended equation to improve estimation of drugdrug interaction
Drugs absorbed into the body are metabolized and thus removed by enzymes from several organs like the liver. How fast a drug is cleared out of the system can be affected by other drugs that are taken together because added substance can increase the amount of enzyme secretion in the body. This dramatically decreases the concentration of a drug, reducing its efficacy, often leading to the failure of having any effect at all. Therefore, accurately predicting the clearance rate in the presence of drugdrug interaction* is critical in the process of drug prescription and development of a new drug in order to ensure its efficacy and/or to avoid unwanted sideeffects. *Drugdrug interaction: In terms of metabolism, drugdrug interaction is a phenomenon in which one drug changes the metabolism of another drug to promote or inhibit its excretion from the body when two or more drugs are taken together. As a result, it increases the toxicity of medicines or causes loss of efficacy. Since it is practically impossible to evaluate all interactions between new drug candidates and all marketed drugs during the development process, the FDA recommends indirect evaluation of drug interactions using a formula suggested in their guidance, first published in 1997, revised in January of 2020, in order to evaluate drug interactions and minimize side effects of having to use more than one type of drugs at once. The formula relies on the 110yearold MichaelisMenten (MM) model, which has a fundamental limit of making a very broad and groundless assumption on the part of the presence of the enzymes that metabolizes the drug. While MM equation has been one of the most widely known equations in biochemistry used in more than 220,000 published papers, the MM equation is accurate only when the concentration of the enzyme that metabolizes the drug is almost nonexistent, causing the accuracy of the equation highly unsatisfactory – only 38 percent of the predictions had less than twofold errors. “To make up for the gap, researcher resorted to plugging in scientifically unjustified constants into the equation,” Professor Jungwoo Chae of Chungnam National University College of Pharmacy said. “This is comparable to having to have the epicyclic orbits introduced to explain the motion of the planets back in the days in order to explain the nowdefunct Ptolemaic theory, because it was 'THE' theory back then.” < (From left) Ph.D. student Yun Min Song (KAIST, cofirst authors), Professor Sang Kyum Kim (Chungnam National University, cocorresponding author), Jae Kyoung Kim, CI (KAIST, cocorresponding author), Professor Jungwoo Chae (Chungnam National University, cocorresponding author), Ph.D. students Quyen Thi Tran and NgocAnh Thi Vu (Chungnam National University, cofirst authors) > A joint research team composed of mathematicians from the Biomedical Mathematics Group within the Institute for Basic Science (IBS) and the Korea Advanced Institute of Science and Technology (KAIST) and pharmacological scientists from the Chungnam National University reported that they identified the major causes of the FDArecommended equation’s inaccuracies and presented a solution. When estimating the gut bioavailability (Fg), which is the key parameter of the equation, the fraction absorbed from the gut lumen (Fa) is usually assumed to be 1. However, many experiments have shown that Fa is less than 1, obviously since it can’t be expected that all of the orally taken drugs to be completely absorbed by the intestines. To solve this problem, the research team used an “estimated Fa” value based on factors such as the drug’s transit time, intestine radius, and permeability values and used it to recalculate Fg. Also, taking a different approach from the MM equation, the team used an alternative model they derived in a previous study back in 2020, which can more accurately predict the drug metabolism rate regardless of the enzyme concentration. Combining these changes, the modified equation with recalculated Fg had a dramatically increased accuracy of the resulting estimate. The existing FDA formula predicted drug interactions within a 2fold margin of error at the rate of 38%, whereas the accuracy rate of the revised formula reached 80%. “Such drastic improvement in drugdrug interaction prediction accuracy is expected to make great contribution to increasing the success rate of new drug development and drug efficacy in clinical trials. As the results of this study were published in one of the top clinical pharmacology journal, it is expected that the FDA guidance will be revised according to the results of this study.” said Professor Sang Kyum Kim from Chungnam National University College of Pharmacy. Furthermore, this study highlights the importance of collaborative research between research groups in vastly different disciplines, in a field that is as dynamic as drug interactions. “Thanks to the collaborative research between mathematics and pharmacy, we were able to recify the formula that we have accepted to be the right answer for so long to finally grasp on the leads toward healthier life for mankind.,” said Professor Jae Kyung Kim. He continued, “I hope seeing a ‘Kformula’ entered into the US FDA guidance one day.” The results of this study were published in the online edition of Clinical Pharmacology and Therapeutics (IF 7.051), an authoritative journal in the field of clinical pharmacology, on December 15, 2022 (Korean time). Thesis Title: Beyond the MichaelisMenten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction (doi: 10.1002/cpt.2824) < Figure 1. The formula proposed by the FDA guidance for predicting drugdrug interactions (top) and the formula newly derived by the researchers (bottom). AUCR (the ratio of substrate area under the plasma concentrationtime curve) represents the rate of change in drug concentration due to drug interactions. The research team more than doubled the accuracy of drug interaction prediction compared to the existing formula. > < Figure 2. Existing FDA formulas tend to underestimate the extent of drugdrug interactions (gray dots) than the actual measured values. On the other hand, the newly derived equation (red dot) has a prediction rate that is within the error range of 2 times (0.5 to 2 times) of the measured value, and is more than twice as high as the existing equation. The solid line in the figure represents the predicted value that matches the measured value. The dotted line represents the predicted value with an error of 0.5 to 2 times. > For further information or to request media assistance, please contact Jae Kyoung Kim at Biomedical Mathematics Group, Institute for Basic Science (IBS) (jaekkim@ibs.re.kr) or William I. Suh at the IBS Communications Team (willisuh@ibs.re.kr).  About the Institute for Basic Science (IBS) IBS was founded in 2011 by the government of the Republic of Korea with the sole purpose of driving forward the development of basic science in South Korea. IBS has 4 research institutes and 33 research centers as of January 2023. There are eleven physics, three mathematics, five chemistry, nine life science, two earth science, and three interdisciplinary research centers.
2023.01.18
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