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Research Articles

Machine learning for skin permeability prediction: random forest and XG boost regression

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Pages 57-65 | Received 20 Jun 2023, Accepted 09 Nov 2023, Published online: 23 Nov 2023

References

  • Burli A, Law RM, Rodriguez J, et al. Organic compounds percutaneous penetration in vivo in man: relationship to mathematical predictive model. Regul Toxicol Pharmacol. 2020;112:104614. doi: 10.1016/j.yrtph.2020.104614.
  • Zhang K, Abraham MH, Liu X. An equation for the prediction of human skin permeability of neutral molecules, ions and ionic species. Int J Pharm. 2017;521(1-2):259–266. doi: 10.1016/j.ijpharm.2017.02.059.
  • Kumar M, Sharma A, Mahmood S, et al. Franz diffusion cell and its implication in skin permeation studies. J Dispersion Sci Technol. .Forthcoming. [cited 2023 Mar 29]:[14 p.]. doi: 10.1080/01932691.2023.2188923.
  • Lundborg M, Wennberg CL, Narangifard A, et al. Predicting drug permeability through skin using molecular dynamics simulation. J Control Release. 2018;283:269–279. doi: 10.1016/j.jconrel.2018.05.026.
  • Ita K, Ukaoma M. Progress in the transdermal delivery of antimigraine drugs. J Drug Delivery Sci Technol. 2022;68:103064. doi: 10.1016/j.jddst.2021.103064.
  • Ita K, Ashong S. Percutaneous delivery of antihypertensive agents: advances and challenges. AAPS PharmSciTech. 2020;21(2):56. doi: 10.1208/s12249-019-1583-9.
  • Naseem S, Zushi Y, Nabi D. Development and evaluation of two-parameter linear free energy models for the prediction of human skin permeability coefficient of neutral organic chemicals. J Cheminform. 2021;13(1):25. doi: 10.1186/s13321-021-00503-5.
  • Potts RO, Guy RH. Predicting skin permeability. Pharm Res. 1992;9(5):663–669. doi: 10.1023/a:1015810312465.
  • Moss GP, Cronin MTD. Quantitative structure–permeability relationships for percutaneous absorption: re-analysis of steroid data. Int J Pharm. 2002;238(1-2):105–109. doi: 10.1016/s0378-5173(02)00057-1.
  • Tsakovska I, Pajeva I, Al Sharif M, et al. Quantitative structure-skin permeability relationships. Toxicology. 2017;387:27–42. doi: 10.1016/j.tox.2017.06.008.
  • Potts RO, Guy RH. A predictive algorithm for skin permeability: the effects of molecular size and hydrogen bond activity. Pharm Res. 1995;12(11):1628–1633. doi: 10.1023/a:1016236932339.
  • Degim IT, Pugh WJ, Hadgraft J. Skin permeability data: anomalous results. Int J Pharm. 1998;170(1):129–133. doi: 10.1016/S0378-5173(98)00113-6.
  • Pugh WJ, Degim IT, Hadgraft J. Epidermal permeability-penetrant structure relationships: 4, QSAR of permeant diffusion across human stratum corneum in terms of molecular weight, H-bonding and electronic charge. Int J Pharm. 2000;197(1-2):203–211. doi: 10.1016/s0378-5173(00)00326-4.
  • Sun Y, Brown MB, Prapopoulou M, et al. The application of stochastic machine learning methods in the prediction of skin penetration. Appl Soft Comput. 2011;11(2):2367–2375. doi: 10.1016/j.asoc.2010.08.016.
  • Salma H, Melha YM, Sonia L, et al. Efficient prediction of in vitro piroxicam release and diffusion from topical films based on biopolymers using deep learning models and generative adversarial networks. J Pharm Sci. 2021;110(6):2531–2543. doi: 10.1016/j.xphs.2021.01.032.
  • Abraham MH. Scales of solute hydrogen-bonding: their construction and application to physicochemical and biochemical processes. Chem Soc Rev. 1993;22(2):73–83. doi: 10.1039/cs9932200073.
  • Abraham MH, Acree WEJr. Equations for the transfer of neutral molecules and ionic species from water to organic phases. J Org Chem. 2010;75(4):1006–1015. doi: 10.1021/jo902388n.
  • Abraham MH, Martins F. Human skin permeation and partition: general linear free-energy relationship analyses. J Pharm Sci. 2004;93(6):1508–1523. doi: 10.1002/jps.20070.
  • Abraham MH, Zhao YH. Determination of solvation descriptors for ionic species: hydrogen bond acidity and basicity. J Org Chem. 2004;69(14):4677–4685. doi: 10.1021/jo049766y.
  • Moss GP, Sun Y, Wilkinson SC, et al. The application and limitations of mathematical modelling in the prediction of permeability across mammalian skin and polydimethylsiloxane membranes. J Pharm Pharmacol. 2011;63(11):1411–1427. doi: 10.1111/j.2042-7158.2011.01345.x.
  • Ali S, Abuhmed T, El-Sappagh S, et al. Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy artificial intelligence. Inf Fusion. 2023;99:101805. doi: 10.1016/j.inffus.2023.101805.
  • Bannigan P, Bao Z, Hickman RJ, et al. Machine learning models to accelerate the design of polymeric long-acting injectables. Nat Commun. 2023;14(1):35. doi: 10.1038/s41467-022-35343-w.
  • Ashrafi P, Moss GP, Wilkinson SC, et al. The application of machine learning to the modelling of percutaneous absorption: an overview and guide. SAR QSAR Environ Res. 2015;26(3):181–204. doi: 10.1080/1062936X.2015.1018941.
  • Paul D, Sanap G, Shenoy S, et al. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021;26(1):80–93. doi: 10.1016/j.drudis.2020.10.010.
  • Hou N, Li M, He L, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 2020;18(1):462. doi: 10.1186/s12967-020-02620-5.
  • Kumar M, Kumar D, Garg Y, et al. Marine-derived polysaccharides and their therapeutic potential in wound healing application - a review. Int J Biol Macromol. 2023;253(Pt 6):127331. doi: 10.1016/j.ijbiomac.2023.127331.
  • Kumar M, Hilles AR, Ge Y, et al. A review on polysaccharides mediated electrospun nanofibers for diabetic wound healing: their current status with regulatory perspective. Int J Biol Macromol. 2023;234:123696. doi: 10.1016/j.ijbiomac.2023.123696.
  • Kumar M, Keshwania P, Chopra S, et al. Therapeutic potential of nanocarrier-mediated delivery of phytoconstituents for wound healing: their current status and future perspective. AAPS PharmSciTech. 2023;24(6):155. doi: 10.1208/s12249-023-02616-6.
  • Gao W, Xu F, Zhou Z-H. Towards convergence rate analysis of random forests for classification. Artif Intell. 2022;313:103788. doi: 10.1016/j.artint.2022.103788.
  • Xia Z, Stewart K. A counterfactual analysis of opioid-involved deaths during the COVID-19 pandemic using a spatiotemporal random forest modeling approach. Health Place. 2023;80:102986. doi: 10.1016/j.healthplace.2023.102986.
  • Couronné R, Probst P, Boulesteix A-L. Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinf. 2018;19(1):270. doi: 10.1186/s12859-018-2264-5.
  • Austin AM, Ramkumar N, Gladders B, et al. Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling. BMC Med Res Methodol. 2022;22(1):300. doi: 10.1186/s12874-022-01774-8.
  • Chen X, Ishwaran H. Random forests for genomic data analysis. Genomics. 2012;99(6):323–329. doi: 10.1016/j.ygeno.2012.04.003.
  • Tarwidi D, Pudjaprasetya SR, Adytia D, et al. An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach. MethodsX. 2023;10:102119. doi: 10.1016/j.mex.2023.102119.
  • Dong C, Qiao Y, Shang C, et al. Non-contact screening system based for COVID-19 on XGBoost and logistic regression. Comput Biol Med. 2022;141:105003. doi: 10.1016/j.compbiomed.2021.105003.
  • Khera R, Haimovich J, Hurley NC, et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol. 2021;6(6):633–641. doi: 10.1001/jamacardio.2021.0122.
  • Cattani G. Combining data envelopment analysis and random forest for selecting optimal locations of solar PV plants. Energy AI. 2023;11:100222. doi: 10.1016/j.egyai.2022.100222.
  • Kwak S, Kim J, Ding H, et al. Machine learning prediction of the mechanical properties of γ-TiAl alloys produced using random Forest regression model. J Mater Res Technol. 2022;18:520–530. doi: 10.1016/j.jmrt.2022.02.108.
  • Nam DY, Rhee JK. Assessment of MicroRNAs associated with tumor purity by random Forest regression. Biology. 2022;11(5):787. doi: 10.3390/biology11050787.
  • Trindade PHE, Mello JFSR, Silva NEOF, et al. Improving ovine behavioral pain diagnosis by implementing statistical weightings based on logistic regression and random forest algorithms. Animals. 2022;12(21):2940. doi: 10.3390/ani12212940.
  • Olaniran OR, Abdullah MAA. Bayesian weighted random forest for classification of high-dimensional genomics data. Kuwait J Sci. 2023;50(4):477–484. doi: 10.1016/j.kjs.2023.06.008.
  • Ma X, Chen Z, Chen P, et al. Predicting the utilization factor of blasthole in rock roadways by random forest. Undergr Space. 2023;11:232–245. doi: 10.1016/j.undsp.2023.01.006.
  • Xue L, Liu Y, Xiong Y, et al. A data-driven shale gas production forecasting method based on the multi-objective random forest regression. J Petrol Sci Eng. 2021;196:107801. doi: 10.1016/j.petrol.2020.107801.
  • Dutschmann TM, Baumann K. Evaluating high-variance leaves as uncertainty measure for random forest regression. Molecules. 2021;26(21):6514. doi: 10.3390/molecules26216514.
  • Yuan Y, Han Y, Yap CW, et al. Prediction of drug permeation through microneedled skin by machine learning. Bioeng Transl Med. 2023;n/a(n/a):e10512. doi: 10.1002/btm2.10512.
  • Baba H, Takahara J-I, Yamashita F, et al. Modeling and prediction of solvent effect on human skin permeability using support vector regression and random forest. Pharm Res. 2015;32(11):3604–3617. doi: 10.1007/s11095-015-1720-4.

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