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Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 469769, 9 pages
Research Article

Predictive Models for Maximum Recommended Therapeutic Dose of Antiretroviral Drugs

1School of Pharmacy and Pharmacology, University of KwaZulu-Natal, Durban 4001, South Africa
2School of Medicine, University of Florida, Gainesville, FL 32601, USA

Received 11 September 2011; Revised 4 November 2011; Accepted 18 November 2011

Academic Editor: John Hotchkiss

Copyright © 2012 Michael Lee Branham et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel method for predicting maximum recommended therapeutic dose (MRTD) is presented using quantitative structure property relationships (QSPRs) and artificial neural networks (ANNs). MRTD data of 31 structurally diverse Antiretroviral drugs (ARVs) were collected from FDA MRTD Database or package inserts. Molecular property descriptors of each compound, that is, molecular mass, aqueous solubility, lipophilicity, biotransformation half life, oxidation half life, and biodegradation probability were calculated from their SMILES codes. A training set ( 𝑛 = 2 3 ) was used to construct multiple linear regression and back propagation neural network models. The models were validated using an external test set ( 𝑛 = 8 ) which demonstrated that MRTD values may be predicted with reasonable accuracy. Model predictability was described by root mean squared errors (RMSEs), Kendall's correlation coefficients (tau), P-values, and Bland Altman plots for method comparisons. MRTD was predicted by a 6-3-1 neural network model ( R M S E = 1 3 . 6 7 , t a u = 0 . 6 4 3 , 𝑃 = 0 . 0 3 5 ) more accurately than by the multiple linear regression ( R M S E = 2 7 . 2 7 , t a u = 0 . 7 1 4 , 𝑃 = 0 . 0 1 9 ) model. Both models illustrated a moderate correlation between aqueous solubility of antiretroviral drugs and maximum therapeutic dose. MRTD prediction may assist in the design of safer, more effective treatments for HIV infection.