Development of an ADME and drug–drug interactions knowledge database for the acceleration of drug discovery and development

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Safety issues and harmful pharmacological interactions of nutritional supplements in Duchenne muscular dystrophy: considerations for Standard of Care and emerging virus outbreaks

At the moment, little treatment options are available for Duchenne muscular dystrophy (DMD). The absence of the dystrophin protein leads to a complex cascade of pathogenic events in myofibres, including chronic inflammation and oxidative stress as well as altered metabolism. The attention towards dietary supplements in DMD is rapidly increasing, with the aim to counteract pathology-related alteration in nutrient intake, the consequences of catabolic distress or to enhance the immunological response of patients as nowadays for the COVID-19 pandemic emergency. By definition, supplements do not exert therapeutic actions, although a great confusion may arise in daily life by the improper distinction between supplements and therapeutic compounds. For most supplements, little research has been done and little evidence is available concerning their effects in DMD as well as their preventing actions against infections. Often these are not prescribed by clinicians and patients/caregivers do not discuss the use with their clinical team. Then, little is known about the real extent of supplement use in DMD patients. It is mistakenly assumed that, since compounds are of natural origin, if a supplement is not effective, it will also do no harm. However, supplements can have serious side effects and also have harmful interactions, in terms of reducing efficacy or leading to toxicity, with other therapies. It is therefore pivotal to shed light on this unclear scenario for the sake of patients. This review discusses the supplements mostly used by DMD patients, focusing on their potential toxicity, due to a variety of mechanisms including pharmacodynamic or pharmacokinetic interactions and contaminations, as well as on reports of adverse events. This overview underlines the need for caution in uncontrolled use of dietary supplements in fragile populations such as DMD patients. A culture of appropriate use has to be implemented between clinicians and patients' groups.

In Silico Drug-Target Profiling

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

Fragment-based prediction of cytochromes P450 2D6 and 1A2 inhibition by recursive partitioning

The evaluation of the ADME (absorption, distribution, metabolism, and excretion) properties of drug candidates is an important stage in drug discovery. To speed up the numerous tests carried out on large databases of compounds, the help of robust and accurate in silico filters is increasingly required. We propose here a method to build predictive and interpretable models for the prediction of cytochrome P450 (CYP) 1A2 and 2D6 inhibition using recursive partitioning (RP), a well-known technique for the construction of decision trees. The originality of the work is the use of several descriptions of the molecules in terms of fragments, i.e. the MACCS keys and five in-house fingerprints based on the electron density properties of fragments, employed to draw easily understandable structure-activity models. The classifiers reached performances of 87.5% and 76.5% of prediction on a validation set for CYP1A2 and CYP2D6, respectively. The analysis of the first nodes of the RP trees permits us to highlight some relations between the structural fragments and the inhibition of CYPs.