6 100 80 D TR 60 M tced 40 i d e rp 20 — D R T 0 M l a icn−20 i l C −40 −60 Computational and Mathematical Methods in Medicine +1.96 SD 58.3 Mean 20.4 −1.96 SD −17.4 60 40 D T R dM 20 e t c i d e p r 0 — D T R lM−20 a c i n i l C−40 −60 +1.96 SD 29.8 Mean 1.3 −1.96 SD −27.3 −40 −20 0 20 40 Average of clinical MRTD and predicted MRTD 60 −20 0 20 40 60 Average of clinical MRTD and predicted MRTD 80 (a) (b) Figure 3: Bland Altman plot for method comparisons. Bland Altman plots are shown for (a) multiple linear regression predicted versus clinical MRTD, and (b) neural network model predicted versus clinical MRTD. Horizontal lines are drawn at the mean diﬀerence, and at the limits of agreement. All predicted values were within limits of agreement for both models, althought these limits were more narrow for the neural network model estimates. The plots are useful revealing the relationship between the diﬀerences and the averages, the slight deviation from symmetry in the multiple regression indicates some systematic bias but no possible outliers were identiﬁed. the parameters were log-transformations of the software output but instead used as direct input values for the training set. Although some of the predictor values ranged over 3 orders of magnitude, this did not appear to adversely aﬀect comparisons of the MLR or ANN methods. Furthermore, because of the highly diverse nature of antiretroviral com- pounds in terms of physicochemical descriptors and split- data validation procedure with randomly selected training and test sets instead of cross-validating scheme. It is our intention to adapt the method as new ARV drugs become listed in evolving FDA database. We began our study looking at dose-related adverse ef- fects of commercial antiretroviral drugs or new ARVs in development. Although the appearance of serious long- term metabolic complications, such as cardiovascular distur- bances [35], hyperlipidemia [35, 36], and diabetes, have been extensively reported [35, 37], few reports on computational models to predict these dose-limiting toxicities [38, 39] can be found in the literature. A mathematical model to predict the optimal dosing regimen for AIDS therapy has been reported [40], but in this case, CD4+ cell counts and knowledge of the adherence interval of individual patients is required to adjust the dose. While the model was eﬀective at reducing dose-limiting toxicities in an AIDS patient popu- lation, it cannot be applied to nonapproved ARV formula- tions or to drugs in development. Since the overwhelming majority of anti-HIV drugs demonstrate eﬃcacy over a small range of treatment doses, MRTD predicting models would be beneﬁcial to the drug delivery scientist who needs to design experiments based on the most eﬀective therapeutic dose of the medication [41]. The MRTD is empirically derived from human clinical trials, thus it is a direct measure of the threshold for dose-related adverse eﬀects in humans. Prediction of the MRTD from molecular structure is of importance for both new and existing drugs which may require modiﬁcations to improve their aqueous solubility or bioavailability in vivo. ARVs ﬁt this description also and are excellent subject molecules for predictive modeling to estimate maximum dose. Unfortunately, the number of antiretroviral drugs with established MRTD is yet small, so the development of models speciﬁcally of ARVs is both tedious and rare. The molecular descriptors used in this study were select- ed to represent physicochemical (MW, AlogP, ASol) and bioaccumulation (OxidHL, P[BD], logBioHL) property inﬂuences therapeutic dose. Although molecular weight does not strongly correlate with toxicity of most compounds, the larger the molecular size of a compound, the smaller its membrane permeability and diﬀusion coeﬃcient become [34]. Therefore, compounds with higher weights are less likely to be absorbed, which limits their systemic toxicity. Results of this study predict no correlation between molecu- lar weight and maximum ARV dose. In contrast, bioactivity and drug toxicity almost always increase with increasing lipophilicity. This is due in part to the fact that lipophilic molecules tend to cross cellular membranes more readily increasing exposure and residence in the body. In addition, lipophilic drugs are characterized by increased plasma pro- tein binding, thus an assessment of lipophilicity is almost always included in the physicochemical evaluation of a drug because of its close association with pharmacologic, permeability and potential bioaccumulation [42, 43]. Our