Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2012, Article ID 469769, 9 pages doi:10.1155/2012/469769 Research Article Predictive Models for Maximum Recommended Therapeutic Dose of Antiretroviral Drugs Michael Lee Branham,1 Edward A. Ross,2 and Thirumala Govender1 1 School of Pharmacy and Pharmacology, University of KwaZulu-Natal, Durban 4001, South Africa 2 School of Medicine, University of Florida, Gainesville, FL 32601, USA Correspondence should be addressed to Michael Lee Branham, malimahweh@gmail.com 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 = 23) 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 (RMSE = 13.67, tau = 0.643, P = 0.035) more accurately than by the multiple linear regression (RMSE = 27.27, tau = 0.714, P = 0.019) 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. 1. Introduction Acquired immunodeﬁciency syndrome (AIDS) is a degen- erative disease of the immune and central nervous sys- tems caused by the human immunodeﬁciency virus (HIV). There are an estimated 33.2 million people living with HIV/AIDS globally [1–3]. Of this number, 22.5 million are in Sub-Saharan Africa, which represents 67.8% of the global number [3]. Antiretroviral drugs (ARVs) may be classiﬁed as nucleoside reverse transcriptase inhibitors (NRTIs), nucleotide reverse transcriptase inhibitors (NtRTI), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PI), and more recently as fusion or inte- grase inhibitors [4, 5]. Since most ARVs have low aqueous solubility and poor bioavailability, several alternative drug delivery strategies have been proposed to optimize systemic concentrations [6, 7]. The important biopharmaceutical properties that need to be considered for eﬀective ARV delivery systems might include solubility, pKa, lipophilicity, permeability, stability in biological ﬂuids, gastrointestinal metabolism, and where possible viral reservoir targeting [6, 8, 9]. To overcome suboptimal biopharmaceutical properties, ARVs are often prescribed at high daily doses which increase the occurrence of adverse side eﬀects and toxicities [10, 11]. Combination therapy, comprising at least three anti-HIV drugs, has become a standard treatment of AIDS [12], but here again the potential for adverse side eﬀects and drug- related noncompliance increases. To address these issues, computational methods have been used to predict dose- limiting toxicities of a few antiretroviral drugs [13, 14] or to optimize ARV formulations [15, 16]. The ability to predict maximum therapeutic dose directly from molecular struc- ture is both clinically and scientiﬁcally attractive in terms of treatment management and reducing drug development costs [17]. Unfortunately, such models for drugs used in the treatment of AIDS do not yet exist. Accurate prediction