bioRxivPharmacology & ToxicologyPreprint
Investigation of Molecular Weights and Pharmacokinetic Characteristics of Older and Modern Small Drugs
Abstract
AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSA shift towards higher molecular weight (MW) of drug candidates is anticipated to lead to changed pharmacokinetics (PK), including deteriorated absorption. MethodsThe objective of the study was to investigate changes in MW and PK of drugs over time by comparing MW and measured PK of small drugs (here MW<1500 g/mole) marketed before 2010 (n=277) and MW and in silico predicted (data produced using the ANDROMEDA by Prosilico software) and of small drugs marketed in 2021 (n=28). ResultsApparently, there has been a shift towards higher MW (from 355 to 551 g/mole on average). This has influenced PK-parameters such as unbound fraction (on average approximately halved), fraction excreted renally (on average approximately halved; markedly decreased contribution by active secretion), bile excretion (almost 4-fold increased appearance; now for more than every other drug) and intrinsic metabolic clearance (increased). The very high percentage of modern drugs with (according to in silico predictions) significant renal and biliary excretion and gut-wall extraction, metabolic stability, limited passive intestinal permeability+efflux, limited gastrointestinal dissolution/solubility potential and/or a very low fu increases complexity in predictions and places demands on predictive laboratory and computational methods. ConclusionIncreased MW and changed PK-profiles (increased complexity) with time were observed. This shows the need for updating method set-ups for quantification and prediction of PK-parameters. ANDROMEDA has the capability to predict and optimize human clinical PK-characteristics of modern drug candidates with high accuracy.
- assay: metabolomics
- research focus: drug response / drug discovery
- research focus: variant effect prediction
- research focus: benchmarking
- research focus: software / tool
- approach: statistical model
- approach: machine learning (classical)
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