Development and interpretation of a QSAR model for in vitro breast cancer (MCF-7) cytotoxicity of 2-phenylacrylonitriles

The Arylhydrocarbon Receptor (AhR), a member of the Per-ARNT-SIM transcription factor family, has been as a potential new target to treat breast cancer sufferers. A series of 2-phenylacrylonitriles targeting AhR has been developed that have shown promising and selective activity against cancerous cell lines while sparing normal non-cancerous cells. A quantitative structure–activity relationship (QSAR) modeling approach was pursued in order to generate a predictive model for cytotoxicity to support ongoing synthetic activities and provide important structure-activity information for new structure design. Recent work conducted by us has identified a number of compounds that exhibited false positive cytotoxicity values in the standard MTT assay. This work describes a good quality model that not only predicts the activity of compounds in the MCF-7 breast cancer cell line, but was also able to identify structures that subsequently gave false positive values in the MTT assay by identifying compounds with aberrant biological behavior. This work not only allows the design of future breast cancer cytotoxic activity in vitro, but allows the avoidance of the synthesis of those compounds anticipated to result in anomalous cytotoxic behavior, greatly enhancing the design of such compounds.

Discovery of novel SERCA inhibitors by virtual screening of a large compound library

Two screening protocols based on recursive partitioning and computational ligand docking methodologies, respectively, were employed for virtual screens of a compound library with 345,000 entries for novel inhibitors of the enzyme sarco/endoplasmic reticulum calcium ATPase (SERCA), a potential target for cancer chemotherapy. A total of 72 compounds that were predicted to be potential inhibitors of SERCA were tested in bioassays and 17 displayed inhibitory potencies at concentrations below 100 μM. The majority of these inhibitors were composed of two phenyl rings tethered to each other by a short link of one to three atoms. Putative interactions between SERCA and the inhibitors were identified by inspection of docking-predicted poses and some of the structural features required for effective SERCA inhibition were determined by analysis of the classification pattern employed by the recursive partitioning models.

Identification and characterization of novel sodium/potassium-ATPase inhibitors by virtual screening of a compound database

The medicinal value of cardiac glycoside inhibitors for the treatment of congestive heart failure symptoms stems from their ability to specifically inhibit the ion transport activity of the transmembrane enzyme sodium/potassium-ATPase (Na/K-ATPase) in myocardial cells. In this study, we used the inhibitory potencies of 39 cardiac glycoside analogues for the development of a quantitative structure-activity relationship (QSAR) model for Na/K-ATPase inhibition. In conjunction with a substructure and similarity search, the QSAR model was used to select ten potential inhibitors from a commercial compound database. The inhibitory potencies of these compounds were measured and four were found to be more active than the commonly used inhibitor ouabain. The results of the bioassays were incorporated into a second QSAR model, whose physical interpretation suggested that the nature of substituents in positions 10, 12, and 17 at the cyclopentanoperhydrophenanthrene core of the inhibitors was critical for enzyme inhibition. All descriptors of the QSAR models were conformation-independent, making the search protocol a suitable tool for the rapid virtual screening of large compound databases for novel inhibitors.

Comparison of LC/MS and SFC/MS for screening of a large and diverse library of pharmaceutically relevant compounds

The search for greater speed of analysis has fueled many innovations in high-performance liquid chromatography (HPLC), such as the use of higher pressures and smaller stationary-phase particles, and the development of monolithic columns. Alternatively, one might alter the chromatographic mobile phase. The low viscosity and high diffusivity of the mobile phase in supercritical fluid chromatography (SFC) allows higher flow rates and lower pressure drops than is possible in traditional HPLC. In addition, SFC requires less organic, or aqueous-organic, solvent than LC (important in preparative-scale chromatography) and provides an alternative, normal-phase retention mechanism. But fluids that are commonly used as the main mobile-phase component in SFC, such as CO2, are relatively nonpolar. As a result, SFC is commonly believed to only be applicable to nonpolar and relatively low-polarity compounds. Here we build upon recent work with SFC of polar and ionic compounds and peptides, and we compare the LC/MS and SFC/MS of a diverse library of druglike compounds. A total of 75.0% of the library compounds were eluted and detected by SFC/MS, while 79.4% were eluted and detected by LC/MS. Some samples provided strong peaks that appeared to be related to the purported compound contained in the sample. When these were added to the "hits", the numbers rose to 86.7 and 89.9%, respectively. A total of 3.7% of the samples were observed by SFC/MS, but not by LC/MS, and 8.1% of the samples were observed by LC/MS, but not by SFC/MS. The only compound class that appeared to be consistently detected in LC/MS, but not in SFC/MS under our conditions, consisted of compounds containing a phosphate, a phosphonate, or a bisphosphonate. The SFC/MS method was at least as durable, reliable, and user-friendly as the LC/MS method. The APCI source required less cleaning during the SFC/MS separations than it did during LC/MS.

Identification of latent variables in a semantic odor profile database using principal component analysis

Many classifications of odors have been proposed, but none of them have yet gained wide acceptance. Odor sensation is usually described by means of odor character descriptors. If these semantic profiles are obtained for a large diversity of compounds, the resulting database can be considered representative of odor perception space. Few of these comprehensive databases are publicly available, being a valuable source of information for fragrance research. Their statistical analysis has revealed that the underlying structure of odor space is high dimensional and not governed by a few primary odors. In a new effort to study the underlying sensory dimensions of the multivariate olfactory perception space, we have applied principal component analysis to a database of 881 perfume materials with semantic profiles comprising 82 odor descriptors. The relationships identified between the descriptors are consistent with those reported in similar studies and have allowed their classification into 17 odor classes.

Interpreting Computational Neural Network Quantitative Structure−Activity Relationship Models: A Detailed Interpretation of the Weights and Biases

In this work, we present a methodology to interpret the weights and biases of a computational neural network (CNN) quantitative structure-activity relationship model. The methodology allows one to understand how an input descriptor is correlated to the predicted output by the network. The method consists of two parts. First, the nonlinear transform for a given neuron is linearized. This allows us to determine how a given neuron affects the downstream output. Next, a ranking scheme for neurons in a layer is developed. This allows us to develop interpretations of a CNN model similar in manner to the partial least squares (PLS) interpretation method for linear models described by Stanton. The method is tested on three datasets covering both physical and biological properties. The results of this interpretation method correspond well to PLS interpretations for linear models using the same descriptors as the CNN models, and they are consistent with the generally accepted physical interpretations for these properties.

Development and Use of Hydrophobic Surface Area (HSA) Descriptors for Computer-Assisted Quantitative Structure−Activity and Structure−Property Relationship Studies

A new series of 25 whole-molecule molecular structure descriptors are proposed. The new descriptors are termed Hydrophobic Surface Area, or HSA descriptors, and are designed to capture information regarding the structural features responsible for hydrophobic and hydrophilic intermolecular interactions. The utility of the HSAs in capturing this type of information is demonstrated using two properties that have a known hydrophobic component. The first study involves the modeling of the inhibition of Gram-positive bacteria cell growth of a series of biarylamides. The second application involves the study of the blood-brain barrier penetration of a diverse series of drug molecules. In both cases, the HSAs are shown to effectively capture information related to the hydrophobic components of these two properties. Additional evaluation of the new class of descriptors shows them to be unique in their ability to measure hydrophobic features among a diverse set of conventional structural descriptors. The HSAs are evaluated regarding their sensitivity to conformational changes and are found to be similar in that regard to other widely used molecular descriptors.

Development of a quantitative structure-activity relationship model for inhibition of gram-positive bacterial cell growth by biarylamides

A set of compounds consisting of a new and diverse collection of biarylamides was examined using quantitative structure-activity relationship techniques for the purpose of developing a model to describe inhibition of gram-positive bacterial growth (minimum inhibition concentration). The model was sought in order to obtain insight for designing new molecules. A detailed analysis of the underlying structure-activity relationship helped provide insight concerning which structural features of the molecules modulated the activity of the compounds against gram-positive organisms.

Elution and preliminary structure-retention modeling of polar and ionic substances in supercritical fluid chromatography using volatile ammonium salts as mobile phase additives

Supercritical fluid chromatography has primarily been applied to relatively nonpolar analytes, even when polar organic solvents are used as modifiers. Here, we show that low levels of volatile ammonium salts as mobile-phase additives allow the elution of polar and even ionic organic materials such as sulfonate salts, carboxylate salts, polyamines, and quaternary ammonium salts. Also, volatile ammonium salts are compatible with mass spectrometric detection, in contrast to other common additives. We have performed preliminary structure-activity-relationship modeling for retention in the CO2/methanol/NH4OAc/Deltabond Cyano system. We have developed a three-descriptor model, where one descriptor, the "relative negative charged surface" explains over 61% of the variance in the retention values. We suggest that two mechanisms have the greatest influence on retention in this system. One is related to the presence of a volatile ammonium salt, the other is related to the ability of a molecule to "hide" its atom with the greatest partial negative charge.

On the Physical Interpretation of QSAR Models

Multidimensional quantitative structure-activity models (QSAR) developed using molecular structure descriptors and regression analysis techniques have found wide utility and acceptance. However, it is often difficult to extract a physical interpretation of such models because of the types of descriptors involved and the multidimensional nature of the model. The work described here illustrates a method of model interpretation that employs partial least squares (PLS) analysis. Structure-activity relationship information is derived from the positions of specific sets of structures in the PLS score plots and the weights for each variable in the PLS components. Using these data, information regarding major structure-activity trends, trend exceptions, and unique or outlying observations is easily obtained. Examples of this methodology are illustrated using QSAR equations developed for the inhibition of quinolone-resistant bacterial DNA gyrase and human topoisomerase-II inhibition by a series of quinolone antibacterial agents.

The effect of room-temperature storage on the stability of compounds in DMSO

The stability of approximately 7200 compounds stored as 20-mM DMSO solutions under ambient conditions was monitored for 1 year. Compound integrity was measured by flow injection analysis using positive and negative electrospray ionization mass spectrometry. Each sample was assessed at the beginning of the study, after 12 months of storage, and at a randomized time point between the initial and final time points of the study. The relationship between length of storage and the probability of observing the compound was described by a repeated-measures logistic regression model. The probability of observing the compound was 92% after 3 months of storage at room temperature, 83% after 6 months, and 52% after 1 year in DMSO. An acceptable limit for compound loss and corresponding maximum storage time for samples in DMSO can be determined based on these results.

The effect of freeze/thaw cycles on the stability of compounds in DMSO

A diverse set of 320 compounds from the Procter & Gamble Pharmaceuticals organic compound repository was prepared as 20-mM DMSO solutions and stored at 4 degrees C under argon in pressurized canisters to simulate a low-humidity environment. The plates were subjected to 25 freeze/thaw cycles while being exposed to ambient atmospheric conditions after each thaw to simulate the time and manner by which compound plates are exposed to the atmosphere during typical liquid-handling and high-throughput screening processes. High-performance liquid chromatography-mass spectrometry with evaporative light-scattering detection was used to quantitate the amount of compound remaining after every 5th freeze/thaw cycle. Control plates were stored either at room temperature under argon or at 4 degrees C under argon without freeze/thaw cycling and were evaluated at the midpoint and the endpoint of the study. The study was conducted over a short time period (i.e., 7 weeks) to minimize the effect of compound degradation over time due to the exposure of the compounds to DMSO. The results from this study will be used to determine the maximum number of freeze/thaw cycles that can be achieved while maintaining acceptable compound integrity.

Development and use of a high-throughput bacterial DNA gyrase assay to identify mammalian topoisomerase II inhibitors with whole-cell anticancer activity

A high-throughput screen (HTS) was developed and used to identify inhibitors of bacterial DNA gyrase. Among the validated hits were 53 compounds that also inhibited mammalian topoisomerase II with IC(50) values of <12.5 micro g/mL for 51 of them. Using computational methods, these compounds were subjected to cluster analysis to categorize them according to their chemical and structural properties. Nine compounds from different clusters were tested for their whole-cell inhibitory activity against 3 cancer cell lines-NCI-H460 (lung), MCF7 (breast), and SF-268 (CNS)-at a concentration of 100 micro M. Five compounds inhibited cell growth by >50% for all 3 cell lines tested. These compounds were tested further against a panel of 53 to 57 cell lines representing leukemia, melanoma, colon, CNS, ovarian, renal, prostate, breast, and non-small cell lung cancers. In this assay, PGE-7143417 was found to be the most potent compound, which inhibited the growth of all the cell lines by 50% at a concentration range of 0.31 to 2.58 micro M, with an average of 1.21 micro M. An additional 17 compounds were also tested separately against a panel of 10 cell lines representing melanoma, colon, lung, mammary, ovarian, prostate, and renal cancers. In this assay, 4 compounds-PGE-3782569, PGE-7411516, PGE-2908955, and PGE-3521917-were found to have activity with concentrations for 50% cell growth inhibition in the 0.59 to 3.33, 22.5 to 59.1, 7.1 to >100, and 24.7 to >100 micro M range.

Profiling of drugs for membrane activity using liposomes as an in vitro model system

The increasing size of chemical libraries being analyzed by high-throughput screening results in a growing number of active compounds that need to be assessed before moving forward in the drug development process. As a consequence, more rapid and highly sensitive strategies are required to accelerate the process of drug discovery without increasing the cost. Due to the fact that significant numbers of compounds from combinatorial libraries are hydrophobic in nature, approaches are needed to evaluate the potentialfor these compounds to interfere with the functions of biological membranes. The liposome system was used to detect agents that act as follows: (i) ionophores able to induce specific ion permeability, e.g., valinomycin for K+ and protonophoric uncouplers for H+; (ii) ion antiporters which exchange H+ for other ions, e.g., nigericin; (iii) agents that form low specificity ion channels in the membrane, e.g., gramicidin; and (iv) detergents and other membrane-disrupting agents. We propose using this liposome assay during the drug development process to identify compounds that have membrane activity and, as a consequence, produce a biological effect by altering the physico-chemical properties of the cell membrane rather than interacting with a protein target. Screening of a representative set of biologically-active compounds (198) indicated that the majority of systemic antimicrobial drugs, but not topical drugs, lack membrane activity in this model system.

Charged partial surface area (CPSA) descriptors QSAR applications

The charged partial surface area, or CPSA descriptors were originally designed for use in structure-physical relationship studies to capture information about the features of molecules responsible for polar intermolecular interactions. Since their development, they have found applications in a broad variety of both structure-property and structure-activity relationship studies. In the present work, the CPSA descriptors are examined in more detail, evaluating their characteristics with regard to conformational dependence, sources of partial atomic charges, utility of whole molecule and substructure varieties, and the inclusion or exclusion of explicit hydrogens. Additionally, an examination of the physical interpretation that can be derived from structure-activity relationships that incorporate the CPSA descriptors is made. Most recently, the CPSA descriptors have been found to be practically useful in the study of acute aquatic toxicity where they appear to provide an alternative to LUMO energy level measures for describing global and local electrophilicity in cases of non-covalent molecular interactions. A second example illustrates the ability of the CPSA descriptors to discriminate agonists and antagonists among compounds that bind strongly at the estrogen receptor. While measures of global and local nucleophilicity and interatomic distances are required to explain receptor binding, volumetric parameters, such as CPSAs, were found to be necessary to provide separation between reactivity patterns for agonists and antagonists, all having high binding affinity to estrogen receptor.

Development of a quantitative structure--property relationship model for estimating normal boiling points of small multifunctional organic molecules

Computer-assisted quantitative structure-property relationship techniques are applied in the development of a robust and accurate model of normal boiling points (boiling at 760 mmHg) for a very diverse set of 268 small organic molecules. Most of the molecules included in this study contain two or more functional groups. The final model yields a tight fit to the training set data (R2 = 0.963), with a fit error of 6.5%. More importantly, the model is also shown to perform well in external prediction. The mean prediction error for boiling points for a 78-member external test set was 12.3 degrees C, or 8.3%. A detailed analysis of the small number of compounds that were either outliers or not well predicted illustrates areas for potential improvement of the methodology used.

Application of nearest-neighbor and cluster analyses in pharmaceutical lead discovery

High throughput screening (HTS) programs based on diverse collections of compounds can rapidly identify leads for potential drug candidates. In cases where the compound collection is truly diverse, one may only identify a few compounds of interest. However, where a large number of hits are identified, it becomes necessary to examine the structures to determine the true number of compound classes involved so that follow-up studies may be conducted as efficiently as possible. In this case, cluster analysis is applied to determine the structural relationship among HTS hits. To efficiently expand around the region of the hit (or a class of hits) in chemical space, we have applied nearest neighbors analysis to select additional compounds from collections of a large number of commercial vendors, achieving an average hit rate in excess of 15%. Applying these techniques in a number of different cases, we obtained results that are useful for subsequent investigations of hits from HTS and other relevant molecular structures from the literature.