Table 4.
Performance of computational methods in predicting the pharmacokinetic properties of natural compounds compared to experimental data.
| Method | Example | Experimental Validation | Performance Summary |
|---|---|---|---|
| Quantum Mechanics (QM) | Stereoselectivity of nicotine hydroxylation by CYP2A6 [277] | Yes (retrospective), computed (~97%) vs. wet lab (89–94%) | High agreement |
| LogP estimation for BBB permeability (e.g., caffeine) [278] | Yes (clinical data, retrospective) | Good match with known BBB-crossing compounds | |
| DFT used for C-H bond energy at the main metabolic site (e.g., acetic acid) [279] | Yes (vs. experimentally derived bond energy) | Lower bond dissociation energy at main metabolic site confirmed by experimental data as compared with other C-H bond | |
| Global reactivity of 4-hydroxyisoleucine [280,281] | Indirect (predicted stability vs. independent plasma stability study) | Supported by external experimental data | |
| Molecular Docking | Docking of drugs with CYP2D6 variants [280] | Yes, retrospective correlation (R2 = 0.81–0.92) | High agreement |
| Flavonoids binding to Pgp [281] | Very weak; correlation r = –0.27 to 0.079 | Poor correlation despite otherwise claims | |
| Lignans and flavonoids binding to Pgp [51] | Partial; 2 of 10 flavonoids experimentally confirmed | Partial success (at least 20%) | |
| Abietane diterpenes binding to Pgp [282] | Yes, for 2 hemisynthesis compounds | Good performance for two tested compounds | |
| Pharmacophore Models | URAT1 inhibitors [105] | Yes, 3 flavonoids of 25 hits were active (relatively low potency) | Modest performance |
| CYP2D6 inhibitors [110] | Yes; 42% strong, 33% moderate inhibition | High agreement (75% activity in vitro) | |
| DDIs via CYP1A2, 2C9, and 3A4 enzymes [283] | Yes (vs in vitro results obtained with fluorescence-based P450 microarrays) | 32.1–65.5% depending on model and enzyme | |
| CYP3A4 inhibitors from Tripterygium wilfordii [284] | Ye (vs. in vitro enzyme inhibition assays); 3 of 5 predicted were confirmed | Good agreement | |
| CYP1A2 inhibitors from herbal compounds [285] | Yes; 7 of 12 compounds active | ~58% accuracy for a combined approach (docking + pharmacophore models) | |
| QSAR Models | COMFA/COMSIA for natural phenolics [163] | Yes; retrospective (r2pred = 0.78, 0.70) | Very good agreement |
| Intestinal absorption prediction [286] | Yes; 83% predictions within 2-fold of observed values | Comparable to in vitro method | |
| Drug absorption in rats [287] | Reliability comparable to the Caco-2 and 2/4/A1 cell lines | Very good agreement | |
| Molecular Dynamics (MD) | Withaferin-A and withanone membrane permeability [288] | Yes; imaging based on antibody detection confirmed MD predictions | Excellent agreement |
| Curcumin and quercetin binding to CYP3A4 and displacing CDK inhibitors [289] | Yes; docking, MD, and IC50 (in vitro) | Excellent agreement in several validation approaches | |
| PBPK Models | Oxymatrine dose prediction [290] | Yes; compared to clinical dose | Predicted dose (367 mg TID) aligned with clinical recommendation |
| Prediction of DDIs for hyperforin with sedative-hypnotics in human patients [291] | Yes—model predictions compared with known clinical interactions | Close agreement, all predictions within acceptable margin of error | |
| PK of hydrastine and berberine [292] | Yes—validated against observed clinical data | Close fit to human PK data | |
| PK of single dose and multiple dose administration of piperine [293] | Yes—validated against actual clinical data | All error values below the two-fold acceptance criterion |