The Top 5 Challenges in Drug Bioavailability That Can Be Solved by AI

Drug bioavailability is a critical factor in drug development, as it is essential in ensuring that drugs are absorbed and distributed to the target site in sufficient concentrations to exert their therapeutic effects. Bioavailability can be defined as “the extent and rate to which the active drug ingredient or active moiety from the drug product is absorbed and becomes available at the site of drug action.” There are several critical challenges that can affect bioavailability, including solubility, permeability, drug-drug interactions, and metabolic profile. Traditional methods for addressing these challenges are often time-consuming and expensive, but luckily, Artificial intelligence (AI) can act as a powerful tool to overcome these challenges and improve drug bioavailability.  

1. Solubility, lipophilicity and permeability predictions 

Permeability is a critical factor that determines the bioavailability of a drug, and refers to its ability to pass through biological membranes. Permeability is based on factors like solubility and lipophilicity, as well as pharmacokinetic properties. Poor permeability can lead to low bioavailability, which can compromise the efficacy of a drug. Luckily, by integrating AI-based predictions with experimental measurements to make solubility and permeability predictions, scientists can optimize the selection of drug candidates with desired bioavailability profiles, thereby saving time and money and improving the chances of developing successful drugs.

AI models can be used to predict drug solubility, lipophilicity, and permeability of drug candidates through biological membranes in various physiological environments such the gastrointestinal tract, blood-brain barrier, or skin. To make accurate predictions, these models are trained by utilizing a wide range of data, including structural features and physicochemical properties, and large sets of experimental results obtained through in vitro, animal, and clinical studies. During the training procedure, AI algorithms are employed, leveraging their ability to discern complex patterns and relationships within the above mentioned data. The training process involves dividing the dataset into training, validation, and test sets, where the model learns to predict the properties of drug candidates by adjusting its internal parameters based on the input features and known outcomes of solubility and permeability. Over multiple iterations, the model's performance is continuously evaluated and optimized using the validation set to prevent overfitting and to ensure that it generalizes accurate outcomes from the new data.

2. In silico ADMET (absorption, distribution, metabolism, and excretion) predictions

Another core challenge related to bioavailability in the drug discovery process is predicting the absorption, distribution, metabolism, and excretion (ADME) properties of drug candidates. In silico ADMET predictions harness the power of AI algorithms and computational models to analyze vast datasets of molecular structures, physicochemical properties, and experimental data. These advanced hybrid models enable the identification of potential drug candidates with enhanced bioavailability and favorable pharmacokinetic profiles.

By integrating AI-driven ADMET predictions with traditional experimental methods, scientists can significantly expedite the drug development process, reducing both time and costs. In silico models act as virtual screening tools, rapidly filtering large libraries of compounds and eliminating those with undesirable ADMET properties. This streamlined approach allows pharmaceutical companies to strategically allocate their resources, focusing on the most promising drug candidates and maximizing the efficiency of the drug discovery pipeline.

AI-powered ADMET predictions extend their utility beyond compound screening. They play a pivotal role in optimizing drug formulations and dosage regimens. By simulating the behavior of drugs within the body, AI models can predict how different formulations, such as tablets, capsules, or injectables, impact drug absorption and bioavailability. Armed with this knowledge, scientists can develop formulations that maximize drug efficacy while minimizing adverse effects, ultimately enhancing patient outcomes. ADMET predictions provide invaluable insights into the behavior of drug candidates, accelerating the drug development process, optimizing drug formulations, enabling personalized medicine, and ultimately contributing to the development of safer and more potent drugs for patients.  

3. Drug-drug interactions and adverse reaction predictions

Predicting drug-drug interactions and toxicity is another area relating to bioavailability in which AI can be used to identify patterns and relationships that would be challenging to detect through traditional methods. By leveraging AI, pharmaceutical companies can significantly reduce the risk of adverse drug interactions and enhance the selectivity and safety of their drug candidates.

AI models are trained on extensive datasets of drug-drug interaction information, allowing them to learn from chemical assay data and make accurate predictions about potential interactions between new drug candidates and existing medications. These models consider various factors, including drug structures, binding affinities, metabolic pathways, and clinical outcomes, to identify potential alterations in the drug's pharmacokinetic and metabolic profile and the risk of interaction between drug molecules that may lead to adverse events. Properly trained and validated AI models are capable of generating accurate results indicating the mechanisms of the forecasted pharmacokinetic alterations such as competition for the plasma protein binding sites or inhibition of the key metabolic enzymes that potentially lead to significant changes in the drug target exposure and the length of drug presence in the body compartments. Knowing those alterations before late preclinical and clinical studies helps prioritize drug candidates with a lower risk of drug-drug interaction and avoid potentially serious events at the clinical stages of drug studies. 

It is worth mentioning that clinically significant drug-drug interactions and serious adverse effects do not occur in all patients. Usually, they happen in 1 per 1000 or even rarer. But, even if they do not occur all of the time, their results can be quite serious in terms of patient safety; and, the low probability of DDIs occurring is actually what makes it difficult to catch them during preclinical and clinical trials using traditional methods. 

In addition to predicting drug-drug interactions, AI also plays a crucial role in toxicity predictions. Accurate prediction of the drug-drug interactions allows properly trained AI-models to predict a potential toxic reaction caused by unfavorable changes of the drug disposition in the body leading to overexposure of the drug compound to the primary target. AI models can analyze large datasets of toxicity data, including in vitro assays, animal studies, clinical trials, and literature reports, to identify potential toxic effects associated with interactions between new drug candidates or existing therapeutics. These models can predict various toxicity endpoints, such as organ damage, genotoxicity, and carcinogenicity, enabling pharmaceutical companies to assess the safety of their drug candidates early in the development process. By leveraging AI-based toxicity predictions, pharmaceutical companies can prioritize safer drug candidates, reducing the risk of adverse events and costly drug recalls.

4. Physiologically-based pharmacokinetics (PBPK)

Physiologically-based pharmacokinetics (PBPK) is a cutting-edge modeling technique that offers valuable insights into the intricate processes governing drug absorption, distribution, metabolism, and excretion within the body. Unlike traditional methods, PBPK models leverage physiological principles to meticulously simulate the pharmacokinetic behavior of drugs in various organs and tissues. This sophisticated approach enables researchers to pinpoint the rate-limiting steps that influence drug absorption and elimination, allowing for targeted optimizations.

By employing PBPK models, scientists can meticulously evaluate and compare the pharmacokinetic profiles of different drug candidates, gaining a deeper understanding of their disposition and potential interactions. This knowledge empowers researchers to design optimal drug dosing regimens, ensuring that the bioavailability of drugs is maximized, and therapeutic efficacy is achieved. PBPK models are also instrumental in evaluating the impact of physiological factors, such as age, gender, and disease states, on drug pharmacokinetics, leading to more precise and personalized dosing recommendations.

PBPK models are based on main parameters determining distribution across the body compartments such as drug physicochemical properties and pharmacokinetic parameters such as ability to bind with plasma proteins and red blood cells, and metabolic profile. Apart from them, precise physiological parameters such as compartmental volume and blood flow rate, the chemical composition of different tissues and their kinetic properties are implemented. The presence of detailed physiological parameters in the PBPK models helps to include populational variability in PK modeling as well as characteristics typical of different sexes, age groups, and demographics. 

5. Discovery of bioavailability enhancers

The quest for bioavailability enhancers - substances that improve the absorption and utilization of drugs - poses a fifth significant hurdle in drug development. AI emerges as a game-changer in tackling this challenge as well, offering a powerful toolkit for the discovery of novel bioavailability-enhancing agents.

AI's ability to sift through vast oceans of data and discern intricate patterns holds tremendous potential for bioavailability enhancer discovery. By analyzing extensive datasets of chemical compounds, AI models can detect subtle relationships and properties that may influence bioavailability. This information can then be utilized to predict the bioavailability-enhancing capabilities of existing compounds, guiding researchers toward the most promising candidates for further investigation.

AI's capabilities extend beyond predicting the potential of known compounds. It can also be harnessed to design and synthesize entirely new compounds tailored for enhanced bioavailability. Through the creation of virtual compound libraries and the application of sophisticated algorithms, AI can assess physicochemical properties and predict bioavailability. This in silico approach significantly accelerates the discovery process, enabling researchers to rapidly identify and synthesize compounds with promising bioavailability-enhancing properties. (Explore VeriSIM Life’s AtlasGEN novel drug designer)

AI's virtual screening capabilities further streamline the identification of potential bioavailability enhancers. By simulating interactions between drug candidates and various biological systems, AI can accurately predict absorption and distribution characteristics. This virtual screening process enables researchers to prioritize compounds with favorable interactions, minimizing the need for extensive and time-consuming experimental testing.