Using AI & MIDD to Decode Drug-drug Interactions
1.4.2024
Drug development is an expensive and complex process, and the potential for drug-drug interactions (DDIs) can make it even more difficult. With advances in AI and machine learning, it is now possible to decode DDIs with model-informed drug development, allowing us to anticipate and predict potential interactions before they become a problem - reducing costs associated with drug development and increasing the success rate of clinical trials. Let’s explore how AI-driven approaches to drug development can be used to decode DDIs, why it is important to detect them, as well as how to integrate AI into your strategy of detecting potential DDIs.
Drug-drug interactions are an important possibility in drug development, and one that cannot be overlooked for any candidate seeking regulated market approval. DDI’s refer to the way two or more drugs interact when they are taken together. This interaction can have potentially serious side effects or even lead to death if not monitored properly, making it crucial for drug developers to understand how different drugs will interact with each other.
For instance, some drugs may increase or decrease the effectiveness of others, leading to unexpected results in the body. In addition, some drugs may cause adverse reactions when taken together, such as a dangerous drop in blood pressure or an increased risk of heart attack. Knowing how different drugs will interact with each other is essential for a successful drug development process, because the prescription of multiple medications is a common practice in healthcare today.
The importance of detecting anticipated DDIs early in research and development cannot be understated; without them being detected early on, costly clinical trials could fail due to unforeseen complications from interactions between two or more drugs. Furthermore, failure to detect DDIs prior to market release could result in costly lawsuits against pharmaceutical companies due to disastrously harmful side-effects caused by unintended drug-drug interactions.
Model-informed drug development (MIDD) is a modern approach to pharmaceutical R&D that uses a combination of clinical data, in-vitro studies and mathematical models, sometimes along with AI and machine learning. This method can optimize drug design and reduce the cost and time associated with drug production. It has been estimated that MIDD increases the success rate of clinical trials by up to 30%.
MIDD is particularly helpful when it comes to deciphering DDIs as this technology can anticipate possible interactions between two or more drugs before they enter the market. Furthermore, platforms like BIOiSIM that utilize MIDD help confirm that medications are safe for human use before they are made available on the market. This also means that drugs have a much higher chance of passing clinical trials since detailed predictions can be made regarding how different drugs may interact together in humans.
AI and machine learning enabled drug development can provide tremendous insight into decoding DDI’s and help anticipate and predict potential interactions before they become a problem.
AI's capability to predict ED/EC 50, CyP binding affinity, and target/off-target protein binding affinity is crucial in the context of predicting drug-drug interactions (DDIs). For ED/EC 50, AI can accurately estimate the effective dose or concentration of a drug required to achieve a therapeutic effect, thereby optimizing dosing and reducing the risk of adverse effects. In terms of CyP binding affinity, AI can predict how drugs interact with cytochrome P450 enzymes, crucial for metabolism and elimination, helping to anticipate and mitigate potential DDIs that can affect drug effectiveness or cause toxicity. Finally, predicting target/off-target protein binding affinity aids in understanding both the intended therapeutic interactions and unintended interactions that might lead to adverse events or reduced efficacy. Overall, AI enhances the understanding and management of DDIs, leading to safer and more effective therapeutic strategies.
By leveraging AI and model-informed drug development (a “hybrid” AI approach), it is possible to identify potential DDIs before they occur in the real world.
Computational platforms deploying advanced AI and ML techniques, a big data foundation and state of the art physiologically-based mechanistic models can utilize vast sets of data on the possible effects of DDIs while using AI to assess the relative safety of drug combinations in clinical trials. These models are able to analyze the large datasets quickly and accurately, providing insights into the most likely outcomes for a given DDI. This kind of analysis enables researchers to design better treatments and experiment with different combinations without exposing patients to potentially dangerous drugs or side effects. Such tools can predict and account for possible drug-drug interactions for a particular drug candidate, among other drug discovery-related tasks. This includes identifying new drugs as inhibitors, substrates, inducers of interaction with targets, pathways and enzymes of interest, such as potentially problematic classes, and those identified by the FDA of particular concern. The FDA recognizes DDIs as a critical aspect of drug development to pay attention to and documents its guidelines regarding safety around potential DDIs.
Overall, using AI and model-informed drug development is an effective way of predicting possible DDIs prior to market release. It allows for better informed decisions when designing new treatments as well as reducing costs associated with drug development and increasing the success rate of clinical trials. By leveraging this technology, pharmaceutical companies will be able to create safe medications that provide optimal benefits for both their customers and society as a whole.
Integrating AI into a strategy of detecting potential drug-drug interactions (DDI) is essential for safe and effective drug development. AI-driven models support the analysis of large datasets to predict likely outcomes, ultimately leading to better informed decisions when designing new treatments. By utilizing AI in drug discovery and development, the accuracy and speed of the process can be improved while also reducing costs. Check out our eBook, “How to Evolve from Traditional Model-Informed Drug Discovery & Development to an AI-informed Approach” to learn how to get started today.
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The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
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