Technology

Researchers are refining methods to predict how chemicals interact with human plasma proteins, a critical step in assessing the safety and efficacy of new compounds. A new approach leverages machine learning and a technique called “read-across” to estimate the fraction of a chemical unbound in plasma – a key pharmacokinetic parameter – with improved accuracy and interpretability. This advancement offers a potential alternative to traditional animal testing and could accelerate the development of safer pharmaceuticals and industrial chemicals.

Understanding plasma protein binding is crucial because only the unbound fraction of a chemical can exert pharmacological activity or cause toxic effects. High protein binding can reduce the amount of free drug available, potentially diminishing therapeutic benefits. Conversely, a high unbound fraction could increase the risk of adverse reactions. Accurately predicting this binding affinity, known as the fraction unbound (fu), is therefore a cornerstone of drug development and chemical safety assessment. The focus on predictive modelling of human plasma fraction unbound (fu) represents a significant step towards more efficient and ethical chemical evaluation.

Read-Across and Machine Learning: A Synergistic Approach

The research, detailed in recent publications, centers on combining read-across methodology with machine learning algorithms. Read-across involves using data from structurally similar chemicals to predict the properties of a target chemical. This is particularly useful when experimental data for the target compound is limited or unavailable. The study highlights the power of using a similarity-based read-across (RA) method to generate predictive models. Researchers are focusing on creating Physiologically-Based Kinetic (PBK) modeling using this approach.

Machine learning algorithms are then employed to analyze the relationships between chemical structure and the fraction unbound in plasma. The team at Jadavpur University, as detailed in their published abstract, developed models using computational resources to generate predictions as an alternative to animal experimentation. These models aim to be interpretable, meaning scientists can understand *why* a particular prediction is made, rather than simply receiving a black-box output. This interpretability is vital for building confidence in the predictions and identifying potential areas for further investigation.

Improving Predictive Accuracy and Reducing Reliance on Animal Testing

Traditional methods for determining plasma protein binding often involve laboratory experiments, which can be time-consuming, expensive, and raise ethical concerns regarding animal welfare. The development of accurate in silico (computer-based) models offers a compelling alternative. Recent advancements, including transfer learning strategies – where a model trained on a broad chemical library is fine-tuned for specific applications – are further enhancing predictive capabilities. A study on per- and polyfluoroalkyl substances (PFAS) demonstrates the potential of these techniques.

The European Food Safety Authority (EFSA) is also actively exploring the employ of read-across for chemical safety assessment, recognizing its potential to streamline the evaluation process. According to EFSA guidance, Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) models can predict Absorption, Distribution, Metabolism, and Excretion (ADME) parameters, including the fraction absorbed and the fraction unbound.

Applications in Pharmaceutical Development and Beyond

The implications of this research extend beyond pharmaceutical development. Accurate prediction of plasma protein binding is also crucial for assessing the environmental fate and toxicity of industrial chemicals, pesticides, and other substances. For example, a case study involving Valproic acid and 2-Ethylhexanoic acid utilized PBK read-across to model exposure in pregnant individuals and fetuses, incorporating plasma protein binding data (see parameters incorporated for PBK modelling).

As computational resources continue to expand and machine learning algorithms develop into more sophisticated, these predictive models are poised to play an increasingly important role in chemical safety assessment and drug discovery. The ongoing refinement of these techniques promises a future where chemical development is both more efficient and more protective of human health and the environment.

The continued development and validation of these models will be critical to their widespread adoption. Further research will likely focus on expanding the chemical space covered by these models and improving their ability to handle complex chemical structures. What comes next will be the integration of these predictive tools into regulatory frameworks and industry workflows.

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