Allosteric inhibition of the IRE1α RNase preserves cell viability and function during endoplasmic reticulum stress.

Depending on endoplasmic reticulum (ER) stress levels, the ER transmembrane multidomain protein IRE1α promotes either adaptation or apoptosis. Unfolded ER proteins cause IRE1α lumenal domain homo-oligomerization, inducing trans autophosphorylation that further drives homo-oligomerization of its cytosolic kinase/endoribonuclease (RNase) domains to activate mRNA splicing of adaptive XBP1 transcription factor. However, under high/chronic ER stress, IRE1α surpasses an oligomerization threshold that expands RNase substrate repertoire to many ER-localized mRNAs, leading to apoptosis. To modulate these effects, we developed ATP-competitive IRE1α Kinase-Inhibiting RNase Attenuators-KIRAs-that allosterically inhibit IRE1α's RNase by breaking oligomers. One optimized KIRA, KIRA6, inhibits IRE1α in vivo and promotes cell survival under ER stress. Intravitreally, KIRA6 preserves photoreceptor functional viability in rat models of ER stress-induced retinal degeneration. Systemically, KIRA6 preserves pancreatic ß cells, increases insulin, and reduces hyperglycemia in Akita diabetic mice. Thus, IRE1α powerfully controls cell fate but can itself be controlled with small molecules to reduce cell degeneration.

Evaluation of TOPKAT, Toxtree, and Derek Nexus in Silico Models for Ocular Irritation and Development of a Knowledge-Based Framework To Improve the Prediction of Severe Irritation.

Assessment of ocular irritation is an essential component of any risk assessment. A number of (Q)SARs and expert systems have been developed and are described in the literature. Here, we focus on three in silico models (TOPKAT, BfR rulebase implemented in Toxtree, and Derek Nexus) and evaluate their performance using 1644 in-house and 123 European Centre for Toxicology and Ecotoxicology of Chemicals (ECETOC) compounds with existing in vivo ocular irritation classification data. Overall, the in silico models performed poorly. The best consensus predictions of severe ocular irritants were 52 and 65% for the in-house and ECETOC compounds, respectively. The prediction performance was improved by designing a knowledge-based chemical profiling framework that incorporated physicochemical properties and electrophilic reactivity mechanisms. The utility of the framework was assessed by applying it to the same test sets and three additional publicly available in vitro irritation data sets. The prediction of severe ocular irritants was improved to 73-77% if compounds were filtered on the basis of AlogP_MR (hydrophobicity with molar refractivity). The predictivity increased to 74-80% for compounds capable of preferentially undergoing hard electrophilic reactions, such as Schiff base formation and acylation. This research highlights the need for reliable ocular irritation models to be developed that take into account mechanisms of action and individual structural classes. It also demonstrates the value of profiling compounds with respect to their chemical reactivity and physicochemical properties that, in combination with existing models, results in better predictions for severe irritants.

GPCR ontology: development and application of a G protein-coupled receptor pharmacology knowledge framework.

MOTIVATION: Novel tools need to be developed to help scientists analyze large amounts of available screening data with the goal to identify entry points for the development of novel chemical probes and drugs. As the largest class of drug targets, G protein-coupled receptors (GPCRs) remain of particular interest and are pursued by numerous academic and industrial research projects. RESULTS: We report the first GPCR ontology to facilitate integration and aggregation of GPCR-targeting drugs and demonstrate its application to classify and analyze a large subset of the PubChem database. The GPCR ontology, based on previously reported BioAssay Ontology, depicts available pharmacological, biochemical and physiological profiles of GPCRs and their ligands. The novelty of the GPCR ontology lies in the use of diverse experimental datasets linked by a model to formally define these concepts. Using a reasoning system, GPCR ontology offers potential for knowledge-based classification of individuals (such as small molecules) as a function of the data. AVAILABILITY: The GPCR ontology is available at http://www.bioassayontology.org/bao_gpcr and the National Center for Biomedical Ontologies Web site.

Divergent allosteric control of the IRE1α endoribonuclease using kinase inhibitors.

Under endoplasmic reticulum stress, unfolded protein accumulation leads to activation of the endoplasmic reticulum transmembrane kinase/endoRNase (RNase) IRE1α. IRE1α oligomerizes, autophosphorylates and initiates splicing of XBP1 mRNA, thus triggering the unfolded protein response (UPR). Here we show that IRE1α's kinase-controlled RNase can be regulated in two distinct modes with kinase inhibitors: one class of ligands occupies IRE1α's kinase ATP-binding site to activate RNase-mediated XBP1 mRNA splicing even without upstream endoplasmic reticulum stress, whereas a second class can inhibit the RNase through the same ATP-binding site, even under endoplasmic reticulum stress. Thus, alternative kinase conformations stabilized by distinct classes of ATP-competitive inhibitors can cause allosteric switching of IRE1α's RNase--either on or off. As dysregulation of the UPR has been implicated in a variety of cell degenerative and neoplastic disorders, small-molecule control over IRE1α should advance efforts to understand the UPR's role in pathophysiology and to develop drugs for endoplasmic reticulum stress-related diseases.

The QSPR-THESAURUS: the online platform of the CADASTER project.

The aim of the CADASTER project (CAse Studies on the Development and Application of in Silico Techniques for Environmental Hazard and Risk Assessment) was to exemplify REACH-related hazard assessments for four classes of chemical compound, namely, polybrominated diphenylethers, per and polyfluorinated compounds, (benzo)triazoles, and musks and fragrances. The QSPR-THESAURUS website (http: / /qspr-thesaurus.eu) was established as the project's online platform to upload, store, apply, and also create, models within the project. We overview the main features of the website, such as model upload, experimental design and hazard assessment to support risk assessment, and integration with other web tools, all of which are essential parts of the QSPR-THESAURUS.

A sphingosine 1-phosphate receptor 2 selective allosteric agonist.

Molecular probe tool compounds for the Sphingosine 1-phosphate receptor 2 (S1PR2) are important for investigating the multiple biological processes in which the S1PR2 receptor has been implicated. Amongst these are NF-κB-mediated tumor cell survival and fibroblast chemotaxis to fibronectin. Here we report our efforts to identify selective chemical probes for S1PR2 and their characterization. We employed high throughput screening to identify two compounds which activate the S1PR2 receptor. SAR optimization led to compounds with high nanomolar potency. These compounds, XAX-162 and CYM-5520, are highly selective and do not activate other S1P receptors. Binding of CYM-5520 is not competitive with the antagonist JTE-013. Mutation of receptor residues responsible for binding to the zwitterionic headgroup of sphingosine 1-phosphate (S1P) abolishes S1P activation of the receptor, but not activation by CYM-5520. Competitive binding experiments with radiolabeled S1P demonstrate that CYM-5520 is an allosteric agonist and does not displace the native ligand. Computational modeling suggests that CYM-5520 binds lower in the orthosteric binding pocket, and that co-binding with S1P is energetically well tolerated. In summary, we have identified an allosteric S1PR2 selective agonist compound.

Prediction of aqueous solubility, vapor pressure and critical micelle concentration for aquatic partitioning of perfluorinated chemicals.

The majority of perfluorinated chemicals (PFCs) are of increasing risk to biota and environment due to their physicochemical stability, wide transport in the environment and difficulty in biodegradation. It is necessary to identify and prioritize these harmful PFCs and to characterize their physicochemical properties that govern the solubility, distribution and fate of these chemicals in an aquatic ecosystem. Therefore, available experimental data (10-35 compounds) of three important properties: aqueous solubility (AqS), vapor pressure (VP) and critical micelle concentration (CMC) on per- and polyfluorinated compounds were collected for quantitative structure-property relationship (QSPR) modeling. Simple and robust models based on theoretical molecular descriptors were developed and externally validated for predictivity. Model predictions on selected PFCs were compared with available experimental data and other published in silico predictions. The structural applicability domains (AD) of the models were verified on a bigger data set of 221 compounds. The predicted properties of the chemicals that are within the AD, are reliable, and they help to reduce the wide data gap that exists. Moreover, the predictions of AqS, VP, and CMC of most common PFCs were evaluated to understand the aquatic partitioning and to derive a relation with the available experimental data of bioconcentration factor (BCF).

Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse.

Quantitative structure-activity relationship (QSAR) analyses were performed using the LD(50) oral toxicity data of per- and polyfluorinated chemicals (PFCs) on rodents: rat and mouse. PFCs are studied under the EU project CADASTER which uses the available experimental data for prediction and prioritization of toxic chemicals for risk assessment by using the in silico tools. The methodology presented here applies chemometrical analysis on the existing experimental data and predicts the toxicity of new compounds. QSAR analyses were performed on the available 58 mouse and 50 rat LD(50) oral data using multiple linear regression (MLR) based on theoretical molecular descriptors selected by genetic algorithm (GA). Training and prediction sets were prepared a priori from available experimental datasets in terms of structure and response. These sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the models were verified on 376 per- and polyfluorinated chemicals including those in REACH preregistration list. The rat and mouse endpoints were predicted by each model for the studied compounds, and finally 30 compounds, all perfluorinated, were prioritized as most important for experimental toxicity analysis under the project. In addition, cumulative study on compounds within the AD of all four models, including two earlier published models on LC(50) rodent analysis was studied and the cumulative toxicity trend was observed using principal component analysis (PCA). The similarities and the differences observed in terms of descriptors and chemical/mechanistic meaning encoded by descriptors to prioritize the most toxic compounds are highlighted.

Per- and polyfluoro toxicity (LC(50) inhalation) study in rat and mouse using QSAR modeling.

Fully or partially fluorinated compounds, known as per- and polyfluorinated chemicals are widely distributed in the environment and released because of their use in different household and industrial products. Few of these long chain per- and polyfluorinated chemicals are classified as emerging pollutants, and their environmental and toxicological effects are unveiled in the literature. This has diverted the production of long chain compounds, considered as more toxic, to short chains, but concerns regarding the toxicity of both types of per- and polyfluorinated chemicals are alarming. There are few experimental data available on the environmental behavior and toxicity of these compounds, and moreover, toxicity profiles are found to be different for the types of animals and species used. Quantitative structure-activity relationship (QSAR) is applied to a combination of short and long chain per- and polyfluorinated chemicals, for the first time, to model and predict the toxicity on two species of rodents, rat (Rattus) and mouse (Mus), by modeling inhalation (LC(50)) data. Multiple linear regression (MLR) models using the ordinary-least-squares (OLS) method, based on theoretical molecular descriptors selected by genetic algorithm (GA), were used for QSAR studies. Training and prediction sets were prepared a priori, and these sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the model was verified on a larger set of per- and polyfluorinated chemicals retrieved from different databases and journals. The descriptors involved, the similarities, and the differences observed between models pertaining to the toxicity related to the two species are discussed. Chemometric methods such as principal component analysis (PCA) and multidimensional scaling (MDS) were used to select most toxic compounds from those within the AD of both models, which will be subjected to experimental tests under the EU project CADASTER.

The activities of drug inactive ingredients on biological targets.

Excipients, considered "inactive ingredients," are a major component of formulated drugs and play key roles in their pharmacokinetics. Despite their pervasiveness, whether they are active on any targets has not been systematically explored. We computed the likelihood that approved excipients would bind to molecular targets. Testing in vitro revealed 25 excipient activities, ranging from low-nanomolar to high-micromolar concentration. Another 109 activities were identified by testing against clinical safety targets. In cellular models, five excipients had fingerprints predictive of system-level toxicity. Exposures of seven excipients were investigated, and in certain populations, two of these may reach levels of in vitro target potency, including brain and gut exposure of thimerosal and its major metabolite, which had dopamine D3 receptor dissociation constant K d values of 320 and 210 nM, respectively. Although most excipients deserve their status as inert, many approved excipients may directly modulate physiologically relevant targets.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

Alternative approaches for identifying acute systemic toxicity: Moving from research to regulatory testing.

Acute systemic toxicity testing provides the basis for hazard labeling and risk management of chemicals. A number of international efforts have been directed at identifying non-animal alternatives for in vivo acute systemic toxicity tests. A September 2015 workshop, Alternative Approaches for Identifying Acute Systemic Toxicity: Moving from Research to Regulatory Testing, reviewed the state-of-the-science of non-animal alternatives for this testing and explored ways to facilitate implementation of alternatives. Workshop attendees included representatives from international regulatory agencies, academia, nongovernmental organizations, and industry. Resources identified as necessary for meaningful progress in implementing alternatives included compiling and making available high-quality reference data, training on use and interpretation of in vitro and in silico approaches, and global harmonization of testing requirements. Attendees particularly noted the need to characterize variability in reference data to evaluate new approaches. They also noted the importance of understanding the mechanisms of acute toxicity, which could be facilitated by the development of adverse outcome pathways. Workshop breakout groups explored different approaches to reducing or replacing animal use for acute toxicity testing, with each group crafting a roadmap and strategy to accomplish near-term progress. The workshop steering committee has organized efforts to implement the recommendations of the workshop participants.

Evaluation of OASIS QSAR Models Using ToxCast™ in Vitro Estrogen and Androgen Receptor Binding Data and Application in an Integrated Endocrine Screening Approach.

BACKGROUND: Integrative testing strategies (ITSs) for potential endocrine activity can use tiered in silico and in vitro models. Each component of an ITS should be thoroughly assessed. OBJECTIVES: We used the data from three in vitro ToxCast™ binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and androgen receptor (AR) binding. For stronger binders (described here as AC50 < 1 µM), we also examined the relationship of QSAR predictions of ER or AR binding to the results from 18 ER and 10 AR transactivation assays, 72 ER-binding reference compounds, and the in vivo uterotrophic assay. METHODS: NovaScreen binding assay data for ER (human, bovine, and mouse) and AR (human, chimpanzee, and rat) were used to assess the sensitivity, specificity, concordance, and applicability domain of two OASIS QSAR models. The binding strength relative to the QSAR-predicted binding strength was examined for the ER data. The relationship of QSAR predictions of binding to transactivation- and pathway-based assays, as well as to in vivo uterotrophic responses, was examined. RESULTS: The QSAR models had both high sensitivity (> 75%) and specificity (> 86%) for ER as well as both high sensitivity (92-100%) and specificity (70-81%) for AR. For compounds within the domains of the ER and AR QSAR models that bound with AC50 < 1 µM, the QSAR models accurately predicted the binding for the parent compounds. The parent compounds were active in all transactivation assays where metabolism was incorporated and, except for those compounds known to require metabolism to manifest activity, all assay platforms where metabolism was not incorporated. Compounds in-domain and predicted to bind by the ER QSAR model that were positive in ToxCast™ ER binding at AC50 < 1 µM were active in the uterotrophic assay. CONCLUSIONS: We used the extensive ToxCast™ HTS binding data set to show that OASIS ER and AR QSAR models had high sensitivity and specificity when compounds were in-domain of the models. Based on this research, we recommend a tiered screening approach wherein a) QSAR is used to identify compounds in-domain of the ER or AR binding models and predicted to bind; b) those compounds are screened in vitro to assess binding potency; and c) the stronger binders (AC50 < 1 µM) are screened in vivo. This scheme prioritizes compounds for integrative testing and risk assessment. Importantly, compounds that are not in-domain, that are predicted either not to bind or to bind weakly, that are not active in in vitro, that require metabolism to manifest activity, or for which in vivo AR testing is in order, need to be assessed differently. CITATION: Bhhatarai B, Wilson DM, Price PS, Marty S, Parks AK, Carney E. 2016. Evaluation of OASIS QSAR models using ToxCast™ in vitro estrogen and androgen receptor binding data and application in an integrated endocrine screening approach. Environ Health Perspect 124:1453-1461; http://dx.doi.org/10.1289/EHP184.

Acute Toxicity Prediction in Multiple Species by Leveraging Mechanistic ToxCast Mitochondrial Inhibition Data and Simulation of Oral Bioavailability.

There is great interest in assessing the in vivo toxicity of chemicals using nonanimal alternatives. However, acute mammalian toxicity is not adequately predicted by current in silico or in vitro approaches. Mechanisms of acute toxicity are likely conserved across invertebrate, aquatic, and mammalian species, suggesting that dose-response concordance would be high and in vitro mechanistic data could predict responses in multiple species under conditions of similar bioavailability. We tested this hypothesis by comparing acute toxicity between rat, daphnia, and fish and by comparing their respective acute data to inhibition of mitochondria membrane potential (MMP) using U.S. Environmental Protection Agency ToxCast in vitro high-throughput screening data. Logarithmic scatter plots of acute toxicity data showed a clear relationship between fish, daphnia, and intravenous rat but not oral rat data. Similar plots versus MMP showed a well-delineated upper boundary for fish, daphnia, and intravenous data but were scattered without an upper boundary for rat oral data. Adjustments of acute oral rat toxicity values by simulating fractional absorption and CYP-based metabolism as well as removing compounds with hydrolyzable linkages or flagged as substrates for glucuronidation delineated an upper boundary for rat oral toxicity versus MMP. Mitochondrial inhibition at low concentrations predicted highly acutely toxic chemicals for fish and daphnia but not the rat where toxicity was often attenuated. This use of a single high-throughput screening assay to predict acute toxicity in multiple species represents a milestone and highlights the promise of such approaches but also the need for refined tools to address systemic bioavailability and the impact of limited absorption and first pass metabolism.

Novel selective allosteric and bitopic ligands for the S1P(3) receptor.

Sphingosine 1-phosphate (S1P) is a lysophospholipid signaling molecule that regulates important biological functions, including lymphocyte trafficking and vascular development, by activating G protein-coupled receptors for S1P, namely, S1P(1) through S1P(5). Here, we map the S1P(3) binding pocket with a novel allosteric agonist (CYM-5541), an orthosteric agonist (S1P), and a novel bitopic antagonist (SPM-242). With a combination of site-directed mutagenesis, ligand competition assay, and molecular modeling, we concluded that S1P and CYM-5541 occupy different chemical spaces in the ligand binding pocket of S1P(3). CYM-5541 allowed us to identify an allosteric site where Phe263 is a key gate-keeper residue for its affinity and efficacy. This ligand lacks a polar moiety, and the novel allosteric hydrophobic pocket permits S1P(3) selectivity of CYM-5541 within the highly similar S1P receptor family. However, a novel S1P(3)-selective antagonist, SPM-242, in the S1P(3) pocket occupies the ligand binding spaces of both S1P and CYM-5541, showing its bitopic mode of binding. Therefore, our coordinated approach with biochemical data and molecular modeling, based on our recently published S1P(1) crystal structure data in a highly conserved set of related receptors with a shared ligand, provides a strong basis for the successful optimization of orthosteric, allosteric, and bitopic modulators of S1P(3).

CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals.

Quantitative structure property relationship (QSPR) studies on per- and polyfluorinated chemicals (PFCs) on melting point (MP) and boiling point (BP) are presented. The training and prediction chemicals used for developing and validating the models were selected from Syracuse PhysProp database and literatures. The available experimental data sets were split in two different ways: a) random selection on response value, and b) structural similarity verified by self-organizing-map (SOM), in order to propose reliable predictive models, developed only on the training sets and externally verified on the prediction sets. Individual linear and non-linear approaches based models developed by different CADASTER partners on 0D-2D Dragon descriptors, E-state descriptors and fragment based descriptors as well as consensus model and their predictions are presented. In addition, the predictive performance of the developed models was verified on a blind external validation set (EV-set) prepared using PERFORCE database on 15 MP and 25 BP data respectively. This database contains only long chain perfluoro-alkylated chemicals, particularly monitored by regulatory agencies like US-EPA and EU-REACH. QSPR models with internal and external validation on two different external prediction/validation sets and study of applicability-domain highlighting the robustness and high accuracy of the models are discussed. Finally, MPs for additional 303 PFCs and BPs for 271 PFCs were predicted for which experimental measurements are unknown.

Are Mechanistic and Statistical QSAR Approaches Really Different? MLR Studies on 158 Cycloalkyl-Pyranones.

Two parallel approaches for quantitative structure-activity relationships (QSAR) are predominant in literature, one guided by mechanistic methods (including read-across) and another by the use of statistical methods. To bridge the gap between these two approaches and to verify their main differences, a comparative study of mechanistically relevant and statistically relevant QSAR models, developed on a case study of 158 cycloalkyl-pyranones, biologically active on inhibition (Ki ) of HIV protease, was performed. Firstly, Multiple Linear Regression (MLR) based models were developed starting from a limited amount of molecular descriptors which were widely proven to have mechanistic interpretation. Then robust and predictive MLR models were developed on the same set using two different statistical approaches unbiased of input descriptors. Development of models based on Statistical I method was guided by stepwise addition of descriptors while Genetic Algorithm based selection of descriptors was used for the Statistical II. Internal validation, the standard error of the estimate, and Fisher's significance test were performed for both the statistical models. In addition, external validation was performed for Statistical II model, and Applicability Domain was verified as normally practiced in this approach. The relationships between the activity and the important descriptors selected in all the models were analyzed and compared. It is concluded that, despite the different type and number of input descriptors, and the applied descriptor selection tools or the algorithms used for developing the final model, the mechanistical and statistical approach are comparable to each other in terms of quality and also for mechanistic interpretability of modelling descriptors. Agreement can be observed between these two approaches and the better result could be a consensus prediction from both the models.

A QSAR study of HIV protease inhibitors using theoretical descriptors.

This paper reports the development of quantitative structure-activity relationship (QSAR) models for a set of 170 chemicals using mathematical descriptors which can be calculated directly from molecular structure without the input of any other experimental data. The calculated descriptors include topostructural (TS), topochemical (TC), and quantum chemical (QC). Because the situation is rank deficient i.e. the number of independent variables (descriptors) is larger than the number of compounds, three robust linear statistical modeling methods capable of handling such situations, viz., principal components regression (PCR), partial least square (PLS), and ridge regression (RR) were used for QSAR formulation. Results show that PLS and RR gave better q2 values as compared to the PCR method. Of the three classes of descriptors, the TC indices were the best predictors of anti-HIV activity and the QC indices were the least effective.

Possible allosteric interactions of monoindazole-substituted P2 cyclic urea analogues with wild-type and mutant HIV-1 protease.

Our ongoing efforts to understand the difference in the binding pattern of HIV-1 protease inhibitor (HIVPI) with the wild-type and mutant HIV-1 protease (HIVPR) and to provide mechanistic insight are continued further. We report here the results of a recent quantitative structure-activity relationship (QSAR) study on monoindazole-substituted P2 analogues of cyclic urea HIVPIs. The QSAR models revealed an inverted parabolic relationship between biological activity and calculated molar refractivity (CMR). That is, biological activity first decreases with increase in CMR and at a certain minimum point (inversion point) it suddenly changes and increases with further increase in CMR. CMR is a measure of volume-dependent-polarizability and is an indication of the polar interactions between ligand and receptor. The results seem to be best rationalized by larger molecules inducing a change in a receptor unit that allows for a new mode of interaction. Similar QSAR models were also observed for the biological activity of these molecules tested against a panel of mutant viruses including mutant strains with single amino acid substitution (I84V), double amino acid substitutions (I84V/V82F), and multiple amino acid changes corresponding to mutations observed in clinical isolates of patients treated with Ritonavir((R)). Interestingly the inversion points for these mutant strains were found larger than for wild-type. The subtle but significant difference in the inversion point indicates change in the shape and size of the binding pocket. Earlier QSAR studies have shown that the correlation of biological activity with an inverted parabola is an indicative of the 'allosteric interaction' of the ligands with the receptor. This report presents a detail analysis of these observations.