9 Computational prediction of drug-limited solubility and CYP450-mediated biotransformation
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Anchal Sharma
Abstract
In drug discovery and development, predicting drug properties greatly influences the selection and optimization of lead compounds. Drug solubility and biotransformation by cytochrome P450 (CYP450) enzymes are crucial in determining the success or failure of drug development. Accurate computational prediction of these properties aids in early identification and prioritization of candidates with desirable pharmacokinetic profiles. Limited solubility poses a common challenge in drug development, as poorly soluble compounds often have reduced bioavailability and unfavorable pharmacokinetic and pharmacodynamic profiles. Computational methods can evaluate drug solubility by predicting physicochemical descriptors like lipophilicity, molecular weight, hydrogen bonding potential, and polar surface area. Machine learning algorithms aid in building predictive models for drug solubility. Predicting solubility computationally enables early identification of solubility issues, allowing timely modifications to enhance solubility and increase success chances during clinical development. This chapter discusses about the various models and tools used for CYP450 and solubility prediction with their accuracy rate, specificity, sensitivity and R2 value in contrast to their cross-validation results. By considering predictions of these two properties, researchers can prioritize compounds with favorable solubility profiles and low susceptibility to CYP450-mediated metabolism. This integrated computational approach saves time and resources while improving drug discovery and development efficacy by selecting candidates with higher success likelihood in later stages of the pipeline.
Abstract
In drug discovery and development, predicting drug properties greatly influences the selection and optimization of lead compounds. Drug solubility and biotransformation by cytochrome P450 (CYP450) enzymes are crucial in determining the success or failure of drug development. Accurate computational prediction of these properties aids in early identification and prioritization of candidates with desirable pharmacokinetic profiles. Limited solubility poses a common challenge in drug development, as poorly soluble compounds often have reduced bioavailability and unfavorable pharmacokinetic and pharmacodynamic profiles. Computational methods can evaluate drug solubility by predicting physicochemical descriptors like lipophilicity, molecular weight, hydrogen bonding potential, and polar surface area. Machine learning algorithms aid in building predictive models for drug solubility. Predicting solubility computationally enables early identification of solubility issues, allowing timely modifications to enhance solubility and increase success chances during clinical development. This chapter discusses about the various models and tools used for CYP450 and solubility prediction with their accuracy rate, specificity, sensitivity and R2 value in contrast to their cross-validation results. By considering predictions of these two properties, researchers can prioritize compounds with favorable solubility profiles and low susceptibility to CYP450-mediated metabolism. This integrated computational approach saves time and resources while improving drug discovery and development efficacy by selecting candidates with higher success likelihood in later stages of the pipeline.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- 1 Historical development of computer-aided drug design 1
- 2 Lead-hit-based methods for drug design and ligand identification 23
- 3 Virtual screening tools in ligand and receptor-based drug design 51
- 4 State-of-the-art modeling techniques in performing docking algorithms and scoring 65
- 5 Design of computational chiral compounds for drug discovery and development 81
- 6 Role of integrated bioinformatics in structure-based drug design 91
- 7 Molecular recognizable tools in X-ray crystallography in computer-aided drug design 133
- 8 Design of target hit molecules using molecular dynamic simulations: special key aspects of GROMACS or Role of molecular dynamic simulations in designing a hit molecule for drug discovery 151
- 9 Computational prediction of drug-limited solubility and CYP450-mediated biotransformation 175
- 10 Recent advancement in binding free-energy calculation 211
- 11 Role of structural genomics in drug discovery 243
- 12 Unlocking therapeutic potential: computational approaches for enzyme inhibition discovery 295
- 13 Role of spectroscopy in drug discovery 319
- 14 Computer-aided design of peptidomimetic therapeutics 351
- 15 Developing safer therapeutic agents through toxicity prediction 379
- 16 Identifying prominent molecular targets in the fight against drug resistance 403
- Index 429
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- 1 Historical development of computer-aided drug design 1
- 2 Lead-hit-based methods for drug design and ligand identification 23
- 3 Virtual screening tools in ligand and receptor-based drug design 51
- 4 State-of-the-art modeling techniques in performing docking algorithms and scoring 65
- 5 Design of computational chiral compounds for drug discovery and development 81
- 6 Role of integrated bioinformatics in structure-based drug design 91
- 7 Molecular recognizable tools in X-ray crystallography in computer-aided drug design 133
- 8 Design of target hit molecules using molecular dynamic simulations: special key aspects of GROMACS or Role of molecular dynamic simulations in designing a hit molecule for drug discovery 151
- 9 Computational prediction of drug-limited solubility and CYP450-mediated biotransformation 175
- 10 Recent advancement in binding free-energy calculation 211
- 11 Role of structural genomics in drug discovery 243
- 12 Unlocking therapeutic potential: computational approaches for enzyme inhibition discovery 295
- 13 Role of spectroscopy in drug discovery 319
- 14 Computer-aided design of peptidomimetic therapeutics 351
- 15 Developing safer therapeutic agents through toxicity prediction 379
- 16 Identifying prominent molecular targets in the fight against drug resistance 403
- Index 429
