MI-QSAR models for prediction of corneal permeability of organic compounds

Drug delivery to the anterior segment of the eye: A review of current and future treatment strategies

Research in the development of ophthalmic drug formulations and innovative technologies over the past few decades has been directed at improving the penetration of medications delivered to the eye. Currently, approximately 90% of all ophthalmic drug formulations (e.g. liposomes, micelles) are applied as eye drops. The major challenge of topical eye drops is low bioavailability, need for frequent instillation due to the short half-life, poor drug solubility, and potential side effects. Recent research has been focused on improving topical drug delivery devices by increasing ocular residence time, overcoming physiological and anatomical barriers, and developing medical devices and drug formulations to increase the duration of action of the active drugs. Researchers have developed innovative technologies and formulations ranging from sub-micron to macroscopic size such as prodrugs, enhancers, mucus-penetrating particles (MPPs), therapeutic contact lenses, and collagen corneal shields. Another approach towards the development of effective topical drug delivery is embedding therapeutic formulations in microdevices designed for sustained release of the active drugs. The goal is to optimize the delivery of ophthalmic medications by achieving high drug concentration with prolonged duration of action that is convenient for patients to administer.

Applying machine learning techniques for ADME-Tox prediction: a review

INTRODUCTION

Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety were considered some of the major causes of clinical failures in the development of new chemical entities. In this context, machine learning (ML) techniques have been often used in ADME-Tox studies due to the existence of compounds with known pharmacokinetic properties available for generating predictive models.

AREAS COVERED

This review examines the growth in the use of some ML techniques in ADME-Tox studies, in particular supervised and unsupervised techniques. Also, some critical points (e.g., size of the data set and type of output variable) must be considered during the generation of models that relate ADME-Tox properties and biological activity.

EXPERT OPINION

ML techniques have been successfully employed in pharmacokinetic studies, helping the complex process of designing new drug candidates from the use of reliable ML models. An application of this procedure would be the prediction of ADME-Tox properties from studies of quantitative structure-activity relationships or the discovery of new compounds from a virtual screening using filters based on results obtained from ML techniques.

Reviewing ligand-based rational drug design: the search for an ATP synthase inhibitor

Following major advances in the field of medicinal chemistry, novel drugs can now be designed systematically, instead of relying on old trial and error approaches. Current drug design strategies can be classified as being either ligand- or structure-based depending on the design process. In this paper, by describing the search for an ATP synthase inhibitor, we review two frequently used approaches in ligand-based drug design: The pharmacophore model and the quantitative structure-activity relationship (QSAR) method. Moreover, since ATP synthase ligands are potentially useful drugs in cancer therapy, pharmacophore models were constructed to pave the way for novel inhibitor designs.

Development of novel in silico model to predict corneal permeability for congeneric drugs: a QSPR approach

This study was undertaken to determine in vivo permeability coefficients for fluoroquinolones and to assess its correlation with the permeability derived using reported models in the literature. Further, the aim was to develop novel QSPR model to predict corneal permeability for fluoroquinolones and test its suitability on other training sets. The in vivo permeability coefficient was determined using cassette dosing (N-in-One) approach for nine fluoroquinolones (norfloxacin, ciprofloxacin, lomefloxacin, ofloxacin, levofloxacin, sparfloxacin, pefloxacin, gatifloxacin, and moxifloxacin) in rabbits. The correlation between corneal permeability derived using in vivo studies with that derived from reported models was determined. Novel QSPR-based model was developed using in vivo corneal permeability along with other molecular descriptors. The suitability of developed model was tested on β-blockers (n = 15). The model showed better prediction of corneal permeability for fluoroquinolones (r(2) > 0.9) as well as β-blockers (r(2) > 0.6). The newly developed QSPR model based upon in vivo generated data was found suitable to predict corneal permeability for fluoroquinolones as well as other sets of compounds.

Prediction of human skin permeability using artificial neural network (ANN) modeling

AIM

To develop an artificial neural network (ANN) model for predicting skin permeability (log K(p)) of new chemical entities.

METHODS

A large dataset of 215 experimental data points was compiled from the literature. The dataset was subdivided into 5 subsets and 4 of them were used to train and validate an ANN model. The same 4 datasets were also used to build a multiple linear regression (MLR) model. The remaining dataset was then used to test the 2 models. Abraham descriptors were employed as inputs into the 2 models. Model predictions were compared with the experimental results. In addition, the relationship between log K(p) and Abraham descriptors were investigated.

RESULTS

The regression results of the MLR model were n=215, determination coefficient (R(2))=0.699, mean square error (MSE)=0.243, and F=493.556. The ANN model gave improved results with n=215, R(2)=0.832, MSE=0.136, and F=1050.653. The ANN model suggests that the relationship between log K(p) and Abraham descriptors is non-linear.

CONCLUSION

The study suggests that Abraham descriptors may be used to predict skin permeability, and the ANN model gives improved prediction of skin permeability.