Prediction of gastro-intestinal absorption using multivariate adaptive regression splines

Application of principal component analysis-multivariate adaptive regression splines for the simultaneous spectrofluorimetric determination of dialkyltins in micellar media

A new multicomponent analysis method, based on principal component analysis-multivariate adaptive regression splines (PC-MARS) is proposed for the determination of dialkyltin compounds. In Tween-20 micellar media, dimethyl and dibutyltin react with morin to give fluorescent complexes with the maximum emission peaks at 527 and 520nm, respectively. The spectrofluorimetric matrix data, before building the MARS models, were subjected to principal component analysis and decomposed to PC scores as starting points for the MARS algorithm. The algorithm classifies the calibration data into several groups, in each a regression line or hyperplane is fitted. Performances of the proposed methods were tested in term of root mean square errors of prediction (RMSEP), using synthetic solutions. The results show the strong potential of PC-MARS, as a multivariate calibration method, to be applied to spectral data for multicomponent determinations. The effect of different experimental parameters on the performance of the method were studied and discussed. The prediction capability of the proposed method compared with GC-MS method for determination of dimethyltin and/or dibutyltin.

Influence of molecular properties on oral bioavailability of lipophilic drugs - mapping of bulkiness and different measures of polarity

The biopharmaceutical assessment of new drug candidates based on their chemical structure is important in drug discovery and development. The scope of this study is to focus on lipophilic drugs and to clarify the role of their chemical predictors on oral bioavailability in humans. First their chemical properties were calculated from molecular modeling and the bioavailability data was obtained from the literature. The data was then analyzed by a partial least square method including non-linear terms. Significant coefficients were identified from a group of polarity- and solubility-related properties. Contour plots were constructed mapping molecular weight together with different polarity factors. Depending on the molecular weight a maximal bioavailability was found at solubility parameters of about 31-35 (J/cm(3))(1/2) and HLB values of roughly 4-12. The mapping of lipophilic drugs also revealed that a solubility parameter of less than 20 (J/cm(3))(1/2) or a HLB of smaller than unity is critical for the drug-likeness of new compounds.

The use of machine learning and nonlinear statistical tools for ADME prediction

Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.

Ionization-specific analysis of human intestinal absorption

This study presents a mechanistic QSAR analysis of human intestinal absorption of drugs and drug-like compounds using a data set of 567 %HIA values. Experimental data represent passive diffusion across intestinal membranes, and are considered to be reasonably free of carrier-mediated transport or other unwanted effects. A nonlinear model was developed relating %HIA to physicochemical properties of drugs (lipophilicity, ionization, hydrogen bonding, and molecular size). The model describes ion-specific intestinal permeability of drugs by both transcellular and paracellular routes, and also accounts for unstirred water layer effects. The obtained model was validated on two external data sets consisting of in vivo human jejunal permeability coefficients (P(eff)) and absorption rate constants (K(a)). Validation results demonstrate good predictive power of the model (RMSE = 0.35-0.45 log units for log K(a) and log P(eff)). High prediction accuracy together with clear physicochemical interpretation (log P, pK(a)) makes this model particularly suitable for use in property-based drug design.

Shuffling multivariate adaptive regression splines and adaptive neuro-fuzzy inference system as tools for QSAR study of SARS inhibitors

In this work, the inhibitory activity of pyridine N-oxide derivatives against human severe acute respiratory syndrome (SARS) is predicted in terms of quantitative structure–activity relationship (QSAR) models. These models were developed with the aid of multivariate adaptive regression spline (MARS) and adaptive neuro-fuzzy inference system (ANFIS) combined with shuffling cross-validation technique. A shuffling MARS algorithm is utilized to select the most important variables in QSAR modeling and then these variables were used as inputs of ANFIS to predict SARS inhibitory activities of pyridine N-oxide derivatives. A data set of 119 drug-like compounds was coded with over hundred calculated meaningful molecular descriptors. The best descriptors describing the inhibition mechanism were solvation connectivity index, length to breadth ratio, relative negative charge, harmonic oscillator of aromatic index, average molecular weight and total path count. These parameters are among topological, electronic, geometric, constitutional and aromaticity descriptors. The statistical parameters of R² and root mean square error (RMSE) are 0.884 and 0.359, respectively. The accuracy and robustness of shuffling MARS–ANFIS model in predicting inhibition behavior of pyridine N-oxide derivatives (pIC₅₀) was illustrated using leave-one-out and leave-multiple-out cross-validation techniques and also by Y-randomization. Comparison of the results of the proposed model with those of GA-PLS-ANFIS shows that the shuffling MARS–ANFIS model is superior and can be considered as a tool for predicting the inhibitory behavior of SARS drug-like molecules.

Reconstructing population exposures to environmental chemicals from biomarkers: challenges and opportunities

A conceptual/computational framework for exposure reconstruction from biomarker data combined with auxiliary exposure-related data is presented, evaluated with example applications, and examined in the context of future needs and opportunities. This framework employs physiologically based toxicokinetic (PBTK) modeling in conjunction with numerical "inversion" techniques. To quantify the value of different types of exposure data "accompanying" biomarker data, a study was conducted focusing on reconstructing exposures to chlorpyrifos, from measurements of its metabolite levels in urine. The study employed biomarker data as well as supporting exposure-related information from the National Human Exposure Assessment Survey (NHEXAS), Maryland, while the MENTOR-3P system (Modeling ENvironment for TOtal Risk with Physiologically based Pharmacokinetic modeling for Populations) was used for PBTK modeling. Recently proposed, simple numerical reconstruction methods were applied in this study, in conjunction with PBTK models. Two types of reconstructions were studied using (a) just the available biomarker and supporting exposure data and (b) synthetic data developed via augmenting available observations. Reconstruction using only available data resulted in a wide range of variation in estimated exposures. Reconstruction using synthetic data facilitated evaluation of numerical inversion methods and characterization of the value of additional information, such as study-specific data that can be collected in conjunction with the biomarker data. Although the NHEXAS data set provides a significant amount of supporting exposure-related information, especially when compared to national studies such as the National Health and Nutrition Examination Survey (NHANES), this information is still not adequate for detailed reconstruction of exposures under several conditions, as demonstrated here. The analysis presented here provides a starting point for introducing improved designs for future biomonitoring studies, from the perspective of exposure reconstruction; identifies specific limitations in existing exposure reconstruction methods that can be applied to population biomarker data; and suggests potential approaches for addressing exposure reconstruction from such data.

Drug absorption modeling as a tool to define the strategy in clinical formulation development

The purpose of this mini review is to discuss the use of physiologically-based drug absorption modeling to guide the formulation development. Following an introduction to drug absorption modeling, this article focuses on the preclinical formulation development. Case studies are presented, where the emphasis is not only the prediction of absolute exposure values, but also their change with altered input values. Sensitivity analysis of technologically relevant parameters, like the drug's particle size, dose and solubility, is presented as the basis to define the clinical formulation strategy. Taking the concept even one step further, the article shows how the entire design space for drug absorption can be constructed. This most accurate prediction level is mainly foreseen once clinical data is available and an example is provided using mefenamic acid as a model drug. Physiologically-based modeling is expected to be more often used by formulators in the future. It has the potential to become an indispensable tool to guide the formulation development of challenging drugs, which will help minimize both risks and costs of formulation development.

Prodrugs: design and clinical applications

Prodrugs are bioreversible derivatives of drug molecules that undergo an enzymatic and/or chemical transformation in vivo to release the active parent drug, which can then exert the desired pharmacological effect. In both drug discovery and development, prodrugs have become an established tool for improving physicochemical, biopharmaceutical or pharmacokinetic properties of pharmacologically active agents. About 5-7% of drugs approved worldwide can be classified as prodrugs, and the implementation of a prodrug approach in the early stages of drug discovery is a growing trend. To illustrate the applicability of the prodrug strategy, this article describes the most common functional groups that are amenable to prodrug design, and highlights examples of prodrugs that are either launched or are undergoing human trials.

Applications of artificial intelligence systems in the analysis of epidemiological data

A brief review of the germane literature suggests that the use of artificial intelligence (AI) statistical algorithms in epidemiology has been limited. We discuss the advantages and disadvantages of using AI systems in large-scale sets of epidemiological data to extract inherent, formerly unidentified, and potentially valuable patterns that human-driven deductive models may miss.

Exploration of linear modelling techniques and their combination with multivariate adaptive regression splines to predict gastro-intestinal absorption of drugs

In general, linear modelling techniques such as multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS), are used to model QSAR data. This type of data can be very complex and linear modelling techniques often model only a limited part of the information captured in the data. In this study, it was tried to combine linear techniques with the flexible non-linear technique multivariate adaptive regression splines (MARS). Models were built using an MLR model, combined with either a stepwise procedure or a genetic algorithm for variable selection, a PCR model or a PLS model as starting points for the MARS algorithm. The descriptive and predictive power of the models was evaluated in a QSAR context and compared to the performances of the individual linear models and the single MARS model. In general, the combined methods resulted in significant improvements compared to the linear models and can be considered valuable techniques in modelling complex QSAR data. For the used data set the best model was obtained using a combination of PLS and MARS. This combination resulted in a model with a Pearson correlation coefficient of 0.90 and a cross-validation error, evaluated with 10-fold cross-validation of 9.9%, pointing at good descriptive and high predictive properties.

Evaluation of chromatographic descriptors for the prediction of gastro-intestinal absorption of drugs

The use of chromatographic descriptors in QSAR was evaluated. Therefore, retentions were measured on an immobilized artificial membrane system, 2 micellar liquid chromatography systems and 17 orthogonal or disimilar reversed-phase liquid chromatographic systems. It was investigated whether it was possible to model gastro-intestinal absorption as a function of chromatographic retentions applying two linear and one non-linear multivariate modeling technique. In a second step it was evaluated if models built with theoretical descriptors could be improved by adding the measured retention factors to the data set of descriptive variables. It was seen that gastro-intestinal absorption could be modelled in function of chromatographic retention using the non-linear modeling technique multivariate adaptive regression splines (MARS). The best models were obtained using a combination of theoretical and chromatographic descriptors with MARS as modeling technique.