Predicting drug pharmacokinetic properties using molecular interaction fields and SIMCA

We have developed a method that combines molecular interaction fields with soft independent modeling of class analogy (SIMCA) Wold:1977 to predict pharmacokinetic drug properties. Several additional considerations to those made in traditional QSAR are required in order to develop a successful QSPR strategy that is capable of accommodating the many complex factors that contribute to key pharmacokinetic properties such as ADME (absorption, distribution, metabolism, and excretion) and toxicology. An accurate prediction of oral bioavailability, for example, requires that absorption and first-pass hepatic elimination both be taken into consideration. To accomplish this, general properties of molecules must be related to their solubility and ability to penetrate biological membranes, and specific features must be related to their particular metabolic and toxicological profiles. Here we describe a method, which is applicable to structurally diverse data sets while utilizing as much detailed structural information as possible. We address the issue of the molecular alignment of a structurally diverse set of compounds using idiotropic field orientation (IFO), a generalization of inertial field orientation Clark:1998. We have developed a second flavor of this method, which directly incorporates electrostatics into the molecular alignment. Both variations of IFO produce a characteristic orientation for each structure and the corresponding molecular fields can then be analyzed using SIMCA. Models are presented for human intestinal absorption, blood-brain barrier penetration and bioavailability to demonstrate ways in which this tool can be used early in the drug development process to identify leads likely to exhibit poor pharmacokinetic behavior in pre-clinical studies, and we have explored the influence of conformation and molecular field type on the statistical properties of the models obtained.     

A Structural View on Medicinal Chemistry Strategies against Drug Resistance

The natural phenomenon of drug resistance is a widespread issue that hampers the performance of drugs in many major clinical indications. Antibacterial and antifungal drugs are affected, as well as compounds for the treatment of cancer, viral infections, or parasitic diseases. Despite the very diverse set of biological targets and organisms involved in the development of drug resistance, the underlying molecular mechanisms have been identified to understand the emergence of resistance and to overcome this detrimental process. Detailed structural information on the root causes for drug resistance is nowadays frequently available, so next‐generation drugs can be designed that are anticipated to suffer less from resistance. This knowledge‐based approach is essential for fighting the inevitable occurrence of drug resistance.     

Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction

Quantitative structure–activity relationships (QSAR) methods are urgently needed for predicting ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties to select lead compounds for optimization at the early stage of drug discovery, and to screen drug candidates for clinical trials. Use of suitable QSAR models ultimately results in lesser time-cost and lower attrition rate during drug discovery and development. In the case of ADME/T parameters, drug metabolism is a key determinant of metabolic stability, drug–drug interactions, and drug toxicity. QSAR models for predicting drug metabolism have undergone significant advances recently. However, most of the models used lack sufficient interpretability and offer poor predictability for novel drugs. In this review, we describe some considerations to be taken into account by QSAR for modeling drug metabolism, such as the accuracy/consistency of the entire data set, representation and diversity of the training and test sets, and variable selection. We also describe some novel statistical techniques (ensemble methods, multivariate adaptive regression splines and graph machines), which are not yet used frequently to develop QSAR models for drug metabolism. Subsequently, rational recommendations for developing predictable and interpretable QSAR models are made. Finally, the recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction, including in vivo hepatic clearance, in vitro metabolic stability, inhibitors and substrates of cytochrome P450 families, are briefly summarized.     

Virtual Screening and Structure Generation Applied to Drug Design

The methods of computer-aided drug design can be divided into two categories according to whether or not the structures of receptors are known1, corresponding to two principal strategies: (1) searching the bio-active ligands against virtual combinatorial libraries and calculating the affinity energy between ligand and receptor by docking ; (2) QSAR and 3D-structure data-mining. 3D-QSAR method is now applied widely to drug discovery, but this method is generally limited to refine the structu…     

A single-cell drug efflux assay in bacteria by using a directly accessible femtoliter droplet array

Active efflux of drugs, such as antibiotics, from a cell is one of the major mechanisms that cause multi-drug resistance in bacteria. Here we report a method to assess drug efflux activity in individual Escherichia coli cells enclosed and isolated in a directly accessible femtoliter droplet array with a fluorogenic compound. The inhibitory effect of a chemical compound on an exogenously expressed efflux pump system from pathogenic bacteria was easily detected at the single-cell level. We also present a proof-of-principle experiment to screen for the gene encoding a drug efflux pump by collecting individual droplets containing single cells in which the drug efflux activity was restored after introduction of the exogenous gene from pathogenic bacteria. Our approach will be a useful tool to screen novel pump inhibitors and efflux pump genes, and to overcome infectious diseases caused by multi-drug-resistant bacteria.     

Unified QSAR & network‐based computational chemistry approach to antimicrobials. II. Multiple distance and triadic census analysis of antiparasitic drugs complex networks

In the previous work, we reported a multitarget Quantitative Structure‐Activity Relationship (mt‐QSAR) model to predict drug activity against different fungal species. This mt‐QSAR allowed us to construct a drug–drug multispecies Complex Network (msCN) to investigate drug–drug similarity (González‐Díaz and Prado‐Prado, J Comput Chem 2008, 29, 656). However, important methodological points remained unclear, such as follows: (1) the accuracy of the methods when applied to other problems; (2) the effect of the distance type used to construct the msCN; (3) how to perform the inverse procedure to study species–species similarity with multidrug resistance CNs (mdrCN); and (4) the implications and necessary steps to perform a substructural Triadic Census Analysis (TCA) of the msCN. To continue the present series with other important problem, we developed here a mt‐QSAR model for more than 700 drugs tested in the literature against different parasites (predicting antiparasitic drugs). The data were processed by Linear Discriminate Analysis (LDA) and the model classifies correctly 93.62% (1160 out of 1239 cases) in training. The model validation was carried out by means of external predicting series; the model classified 573 out of 607, that is, 94.4% of cases. Next, we carried out the first comparative study of the topology of six different drug–drug msCNs based on six different distances such as Euclidean, Chebychev, Manhattan, etc. Furthermore, we compared the selected drug–drug msCN and species–species mdsCN with random networks. We also introduced here the inverse methodology to construct species–species msCN based on a mt‐QSAR model. Last, we reported the first substructural analysis of drug–drug msCN using Triadic Census Analysis (TCA) algorithm. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010     

Discovery of Novel Symmetrical 1,4-Dihydropyridines as Inhibitors of Multidrug-Resistant Protein (MRP4) Efflux Pump for Anticancer Therapy

Despite the development of targeted therapies in cancer, the problem of multidrug resistance (MDR) is still unsolved. Most patients with metastatic cancer die from MDR. Transmembrane efflux pumps as the main cause of MDR have been addressed by developed inhibitors, but early inhibitors of the most prominent and longest known efflux pump P-glycoprotein (P-gp) were disappointing. Those inhibitors have been used without knowledge about the expression of P-gp by the treated tumor. Therefore the use of inhibitors of transmembrane efflux pumps in clinical settings is reconsidered as a promising strategy in the case of the respective efflux pump expression. We discovered novel symmetric inhibitors of the symmetric efflux pump MRP4 encoded by the ABCC4 gene. MRP4 is involved in many kinds of cancer with resistance to anticancer drugs. All compounds showed better activities than the best known MRP4 inhibitor MK571 in an MRP4-overexpressing cell line assay, and the activities could be related to the various substitution patterns of aromatic residues within the symmetric molecular framework. One of the best compounds was demonstrated to overcome the MRP4-mediated resistance in the cell line model to restore the anticancer drug sensitivity as a proof of concept.