Cross-validated Regression Coefficient Research Articles

Development of 2-dimensional and 3-dimensional QSAR models of Indazole derivatives as TTK inhibitors having Anticancer potential

The study aimed to explore the anticancer efficacy of indazole pharmacophore by analyzing a series of 109 derivatives of indazole as Tyrosine Threonine Kinase (TTK) inhibitors through quantitative activity relationship analysis using 2D and 3D QSAR techniques. The best 2D-QSAR model was generated by the MLR method, showing a high correlation coefficient (r2) of 0.9512, and good internal (q2), and external (pred_r2) cross-validation regression coefficients of 0.8998, and 0.8661, respectively. The residual values were modest, indicating good agreement between the observed and predicted pIC50 values, which suggested that the chosen model was predictably accurate. The 3D QSAR model, built using the SWF kNN approach, displayed a high internal cross-validation regression coefficient (q2) of 0.9132. Essential structural features/considerations in developing indazole as prospective anticancer medicines have been suggested. The study provides a reliable and predictive model for the prediction of anticancer activity of indazole derivatives. The identified essential structural features/considerations may be useful for the development of prospective anticancer medicines.

Structure-activity relationships of dihydropyrimidone inhibitors against native and auto-processed human neutrophil elastase

BackgroundHuman neutrophil elastase (HNE) is a key driver of systemic and cardiopulmonary inflammation. Recent studies have established the existence of a pathologically active auto-processed form of HNE with reduced binding affinity against small molecule inhibitors. MethodAutoDock Vina v1.2.0 and Cresset Forge v10 software were used to develop a 3D-QSAR model for a series of 47 DHPI inhibitors. Molecular Dynamics (MD) simulations were carried out using AMBER v18 to study the structure and dynamics of sc (single-chain HNE) and tcHNE (two-chain HNE). MMPBSA binding free energies of the previously reported clinical candidate BAY 85–8501 and the highly active BAY-8040 were calculated with sc and tcHNE. ResultsThe DHPI inhibitors occupy the S1 and S2 subsites of scHNE. The robust 3D-QSAR model showed acceptable predictive and descriptive capability with regression coefficient of r2 = 0.995 and cross-validation regression coefficient q2 = 0.579 for the training set. The key descriptors of shape, hydrophobics and electrostatics were mapped to the inhibitory activity. In auto-processed tcHNE, the S1 subsite undergoes widening and disruption. All the DHPI inhibitors docked with the broadened S1′-S2′ subsites of tcHNE with lower AutoDock binding affinities. The MMPBSA binding free energy of BAY-8040 with tcHNE reduced in comparison with scHNE while the clinical candidate BAY 85–8501 dissociated during MD. Thus, BAY-8040 may have lower inhibitory activity against tcHNE whereas the clinical candidate BAY 85–8501 is likely to be inactive. ConclusionSAR insights gained from this study will aid the future development of inhibitors active against both forms of HNE.

Quantitatively modeling of tetracycline photodegradation in low molecular weight organic acids under simulated sunlight irradiation

As the ubiquitous active components in aquatic environments, low molecular weight organic acids (LMWOAs) have a large influence on the environmental behaviors of contaminants. This research was focused on the effect of different LMWOAs including 11 aliphatic acids and 7 aromatic acids on the photodegradation kinetics of tetracycline (TC), and the development of quantitative structure-activity relationship (QSAR) model. Results showed that TC photodegradation in the presence of LMWOAs fitted pseudo-first-order photolysis kinetics, and the observed photolysis rate constant (kobs) varied from 0.077 to 0.331 h−1. The QSAR model was developed by partial-least-squares (PLS) with using a sequential approach with 25 theoretical molecular descriptors. Four descriptors including ELUMO-EHOMO, ELUMO, CCR and Qmax were found to mechanistically and statistically affect kobs.The high cross validated regression coefficient (Qcum2, 0.898) and high correlation coefficient (R2, 0.908) indicated significantly goodness-of-fit and high robustness of the model. The predicted and observed values with high agreement in the defined applicability domain featured accuracy and feasibility of model. This work provided a robust predictive method for estimating the TC photodegradation in the presence of different structures of LMWOAs.

Rapid Nondestructive Postharvest Potato Freshness and Cultivar Discrimination Assessment

Background: Quality and safety of potato is both cultivar and postharvest management dependent. The precise assessment of freshness and cultivar are complex tasks requiring time-consuming, expensive, and destructive techniques. Method: Potatoes from three commercial cultivars were stored for 5 months at 5 °C. Color and chlorophyll fluorescence were recorded, Red-Green-Blue (R-G-B), Red-Green-Near infrared (R-G-NIR) and Red-Blue-Near infrared (R-B-NIR) digital images, as well as hyperspectral images were acquired both on the external periderm of the tuber and in the inner flesh part. Partial least square regression (PLSR) and discriminant analysis, combined with feature selection techniques were implemented, in order to assess the potato freshness and to classify them into the respective genotypes. Results: The PLSR analysis of visible/near infrared (Vis/NIR) spectra reflectance most reliably predicted potato freshness, with a cross-validated regression coefficient equal to 0.981 and 0.947, as determined by external or internal measurements, respectively. Variance inflation factor, variable importance scores, and genetic algorithms identified specific wavelength regions that mostly affected the accuracy of the model in terms of strongest regression and lowest collinearity and root mean cross validation error. Conclusions: Vis/NIR spectra reflectance data from the skin of the potato tubers may be reliably used in the assessment of postharvest storage life, as well as in the cultivar discrimination process.

QSAR, ADME and docking guided semi-synthesis and in vitro evaluation of 4-hydroxy-α-tetralone analogs for anti-inflammatory activity

The compound 4-hydroxy-α-tetralone (1) is major bioactive secondary metabolite of genus Ammannia (Family- Lythraceae). The compound 1 and its various derivatives are reported to possess anti-tubercular, anti-diabetic, anti-leishmanial and bioenhancing activities. A quantitative structure activity relationship (QSAR) model for predicting anti-inflammatory activity of 4-hydroxy-α-tetralone derivatives against the tumour necrosis factor (TNF)-α, a pro-inflammatory cytokine was developed by non-linear model using artificial neural network (ANN). The regression coefficient (r2) and the leave-one-out cross-validation regression coefficient (LOO rCV2) of the QSAR model were 0.6976 and 0.4016, respectively, while the regression coefficient (r2) for the external set of experimental compounds was 0.835. The 4-hydroxy-α-tetralone virtual derivatives, which showed significant inhibition of TNF-α were subjected to docking and in-silico absorption, distribution, metabolism and excretion (ADME) studies and the results showed similar binding affinity and bioavailability in compliance with the standard drug, Diclofenac. Finally, in order to validate the developed QSAR model, the most and least active virtual derivatives were semi-synthesized, characterized on the basis of their 1H and 13C NMR spectroscopic data and in vitro tested for concentration dependent inhibition of TNF-α. The experimental results obtained, agreed well with the predicted values.

Virtual screening, Docking, ADMET and System Pharmacology studies on Garcinia caged Xanthone derivatives for Anticancer activity

Caged xanthones are bioactive compounds mainly derived from the Garcinia genus. In this study, a structure-activity relationship (SAR) of caged xanthones and their derivatives for anticancer activity against different cancer cell lines such as A549, HepG2 and U251 were developed through quantitative (Q)-SAR modeling approach. The regression coefficient (r2), internal cross-validation regression coefficient (q2) and external cross-validation regression coefficient (pred_r2) of derived QSAR models were 0.87, 0.81 and 0.82, for A549, whereas, 0.87, 0.84 and 0.90, for HepG2, and 0.86, 0.83 and 0.83, for U251 respectively. These models were used to design and screened the potential caged xanthone derivatives. Further, the compounds were filtered through the rule of five, ADMET-risk and synthetic accessibility. Filtered compounds were then docked to identify the possible target binding pocket, to obtain a set of aligned ligand poses and to prioritize the predicted active compounds. The scrutinized compounds, as well as their metabolites, were evaluated for different pharmacokinetics parameters such as absorption, distribution, metabolism, excretion, and toxicity. Finally, the top hit compound 1G was analyzed by system pharmacology approaches such as gene ontology, metabolic networks, process networks, drug target network, signaling pathway maps as well as identification of off-target proteins that may cause adverse reactions.

Quantitative prediction and typical factor effects of phosphorus adsorption on the surface sediments from the intertidal zones of the Yellow River Delta, China

Using 13 sediment physicochemical properties and a partial least squares (PLS) regression method, a predictive model was developed for the phosphorus (P)-adsorption capacity of sediments in the intertidal zones of the Yellow River Delta. The cross-validated regression coefficient (Q2cum=0.823) and correlation coefficient (R2=0.854) indicated significantly high robustness of the model. Moreover, P adsorption characteristics of sediments in the intertidal zones were systematically studied. The maximum adsorption rate (274.80mgkg–1h–1) was seen for sediment of the site around which there was aquaculture, which could have led to a higher organic matter content in the sediment. The mass fraction of clay and silt (<62.5µm) in the sediment of this site was 74%. The P-adsorption capacities ranged from 86.63 to 297.49mgkg–1 for all sites. The quantity of P adsorbed decreased with increasing salinity (2–30), and exhibited an inverted U-trend under the effect of pH (5–11). P adsorption increased with increasing P concentration under oxidation conditions (>400mV), but decreased under reduction conditions (0±100mV). These results could contribute to the restoration and management of intertidal zones.

Molecular docking, QSAR and ADMET studies of withanolide analogs against breast cancer.

Withanolides are a group of pharmacologically active compounds present in most prodigal amounts in roots and leaves of Withania somnifera (Indian ginseng), one of the most important medicinal plants of Indian traditional practice of medicine. Withanolides are steroidal lactones (highly oxygenated C-28 phytochemicals) and have been reported to exhibit immunomodulatory, anticancer and other activities. In the present study, a quantitative structure activity relationship (QSAR) model was developed by a forward stepwise multiple linear regression method to predict the activity of withanolide analogs against human breast cancer. The most effective QSAR model for anticancer activity against the SK-Br-3 cell showed the best correlation with activity (r2=0.93 and rCV2 =0.90). Similarly, cross-validation regression coefficient (rCV2=0.85) of the best QSAR model against the MCF7/BUS cells showed a high correlation (r2=0.91). In particular, compounds CID_73621, CID_435144, CID_301751 and CID_3372729 have a marked antiproliferative activity against the MCF7/BUS cells, while 2,3-dihydrowithaferin A-3-beta-O-sulfate, withanolide 5, withanolide A, withaferin A, CID_10413139, CID_11294368, CID_53477765, CID_135887, CID_301751 and CID_3372729 have a high activity against the Sk-Br-3 cells compared to standard drugs 5-fluorouracil (5-FU) and camptothecin. Molecular docking was performed to study the binding conformations and different bonding behaviors, in order to reveal the plausible mechanism of action behind higher accumulation of active withanolide analogs with β-tubulin. The results of the present study may help in the designing of lead compound with improved activity.

CORROSION INHIBITION OF Q235A STEEL IN ACID MEDIUM USING ISATIN DERIVATIVES: A QSAR STUDY

Quantitative Structure-Activity Relationship (QSAR) study was performed on 10 isatin derivatives which were reportedly used as corrosion inhibitors. Dragon software was used to calculate the molecular descriptors. Partial least square (PLS) method was used to run the regression analysis between the descriptors and the corrosion inhibition efficiencies (IE) of the inhibitors. A predictive QSAR model was developed with a correlation coefficient (r2 cal) of 0.9676. The model validity was assessed through internal and external validation. The results show that cross-validation regression coefficient (r2 cv) and prediction regression coefficient (r2 pred) are 0.8163 and 0.9189, respectively. The model was used to predict the IE for ten isatin derivatives. The results confirm a good stability and predictive ability of the model. Dragon-based descriptors provide a very good description of the corrosion inhibition properties of the inhibitors. The results of the QSAR study were found to be consistent with the experimental data.

Structural Features of Quercetin Derivatives by Using Pharmaco-phore Modeling Approach

Background:Quercetin which is a natural occurring flavonoid, exert a direct pro-apoptotic effect on tumor cells by blocking the growth of several cancer cell lines at different phases of the cell cycle. Quercetin derivatives have attracted considerable attention for their cytotoxity against human cancer cell lines. In this study the derivatives of Quercetin were used for docking followed by pharmacophore modeling for studying the 3D features and configurations responsible for biological activity of structurally diverse compounds.Objective:To develop a model which depicts the crucial structural features responsible for anti-lung cancer activities.Method:A robust pharmacophore developed for the receptor have been analyzed to identify potential areas of selectivity in the hyperspace of 3D pharmacophores that may lead to the discovery of anti-lung cancer drug or such compounds which could serve as templates for the design of new molecules as potential anti lung cancer agents.Results:The generated best pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R2= 0.86 for training set and R2= 0.76 for the test set molecules. The Cross validation regression coefficient is Q2= 0.84 for training set and Q2= 0.5 for test set molecules.Conclusion:The R2and Q2reveals that pharmacophore model provide insights into the structural and chemical features of the EGFR inhibitors of Quercetin derivatives that can be used as lead compound for further synthesis as well as for screening other similar novel inhibitors of EGFR.

The kinetics and QSAR of abiotic reduction of mononitro aromatic compounds catalyzed by activated carbon

The kinetics of abiotic reduction of mono-nitro aromatic compounds (mono-NACs) catalyzed by activated carbon (AC) in an anaerobic system were examined. There were 6 types of substituent groups on nitrobenzene, including methyl, chlorine, amino, carboxyl, hydroxyl and cyanogen groups, at the ortho, meta or para positions. Our results showed that reduction followed pseudo-first order reaction kinetics, and that the rate constant (logkSA) varied widely, ranging between −4.77 and −2.82, depending upon the type and position of the substituent. A quantitative structure–activity relationship (QSAR) model using 15 theoretical molecular descriptors and partial-least-squares (PLS) regression was developed for the reduction rates of mono-NACs catalyzed by AC. The cross-validated regression coefficient (Qcum2, 0.861) and correlation coefficient (R2, 0.898) indicated significantly high robustness of the model. The VIP (variable importance in the projection) values of energy of the lowest unoccupied molecular orbital (ELUMO) and the maximum net atomic charge on the aromatic carbon bound to the nitro group (QC-) were 1.15 and 1.01, respectively. These values indicated that the molecular orbital energies and the atomic net charges might play important roles in the reduction of mono-NACs catalyzed by AC in anaerobic systems.

Quantitative structure-activity relationship for antimalarial activity of artemisinin

The increase in resistance to older drugs and the emergence of new types of infection have created an urgent need for discovery and development of new compounds with antimalarial activity. Quantitative-Structure Activity Relationship (QSAR) methodology has been performed to develop models that correlate antimalarial activity of artemisinin analogs and their molecular structures. In this study, the data set consisted of 197 compounds with their activities expressed as log RA (relative activity). These compounds were randomly divided into training set (n=157) and test set (n=40). The initial stage of the study was the generation of a series of descriptors from three-dimensional representations of the compounds in the data set. Several types of descriptors which include topological, connectivity indices, geometrical, physical properties and charge descriptors have been generated. The number of descriptors was then reduced to a set of relevant descriptors by performing a systematic variable selection procedure which includes zero test, pairwisecorrelation analysis and genetic algorithm (GA). Several models were developed using different combinations of modelling techniques such as multiple linear regression (MLR) and partial least square (PLS) regression. Statistical significance of the final model was characterized by correlation coefficient, r2 and root-mean-square error calibration, RMSEC. The results obtained were comparable to those from previous study on the same data set with r2 values greater than 0.8. Both internal and external validations were carried out to verify that the models have good stability, robustness and predictive ability. The cross-validated regression coefficient (r2cv) and prediction regression coefficient (r2 test) for the external test set were consistently greater than 0.7. The QSAR models developed in this study should facilitate the search for new compounds with antimalarial activity.

Prediction of binding affinities of PCDDs, PCDFs and PCBs using docking-based Comparative Molecular Similarity Indices Analysis

Polychlorinated Dibenzodioxins (PCDDs), Dibenzofurans (PCDFs) and Biphenyls (PCBs) are industrial compounds or byproducts that can cause toxic effects after binding to aryl hydrocarbon receptor (AhR). But the mechanism about PCDDs, PCDFs and PCBs binding to AhR is unclear. To study the interaction and significant amino acid residues in binding of PCDDs, PCDFs and PCBs to AhR, a docking-based Comparative Molecular Similarity Indices Analysis (CoMSIA) was performed on a set of structurally diverse PCDDs, PCDFs and PCBs with known binding affinities. The docking-based CoMSIA model (non-cross-validated regression coefficient of 0.942 and cross-validated regression coefficient of 0.768) was developed and compared with previous report, the presented docking-based CoMSIA model showed good robustness and predictive performance. The obtained docking conformations and predictive CoMSIA model could provide clues to understand key residues and interactions between receptor and compounds of interest.

Alignment-independent QSAR Analysis of SecA Inhibitors

SecA ATPase plays a crucial role in translocation of membrane and secreted polypeptides and proteins in bacteria and therefore a perfect target for novel antimicrobial drug design. Herein, we generated QSAR models with an alignment-independent method. The optimum model obtained for the training set was statistically significant with cross-validation regression coefficient (q2) value of 0.40 and correlation coefficient (r2) value of 0.89. These results suggest that this 3D-QSAR model can be used to guide the development of new SecA inhibitors.

3D-QSAR Study on Acute Toxicity of Halogenated Phenol to Green Fluorescent Protein Using CoMFA and CoMSIA

3D-QSAR studies of halogenated phenols screening for acute toxicity were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Groups’ data has been modeled to obtain an average estimate and a predictive value for ranking and screening purposes. CoMFA and CoMSIA models have given cross-validation regression coefficient (q2) values of more than 0.80 and correlation coefficient (R2) value of more than 0. 96, which validated for their prediction, could be applied to predict unavailable data.

QSAR, Docking and ADMET Studies of Artemisinin Derivatives for Antimalarial Activity Targeting Plasmepsin II, a Hemoglobin-Degrading Enzyme from P. falciparum

This work presents the development of quantitative structure activity relationship (QSAR) model to predict the antimalarial activity of artemisinin derivatives. The structures of the molecules are represented by chemical descriptors that encode topological, geometric, and electronic structure features. Screening through QSAR model suggested that compounds A24, A24a, A53, A54, A62 and A64 possess significant antimalarial activity. Linear model is developed by the multiple linear regression method to link structures to their reported antimalarial activity. The correlation in terms of regression coefficient (r(2)) was 0.90 and prediction accuracy of model in terms of cross validation regression coefficient (rCV(2)) was 0.82. This study indicates that chemical properties viz., atom count (all atoms), connectivity index (order 1, standard), ring count (all rings), shape index (basic kappa, order 2), and solvent accessibility surface area are well correlated with antimalarial activity. The docking study showed high binding affinity of predicted active compounds against antimalarial target Plasmepsins (Plm-II). Further studies for oral bioavailability, ADMET and toxicity risk assessment suggest that compound A24, A24a, A53, A54, A62 and A64 exhibits marked antimalarial activity comparable to standard antimalarial drugs. Later one of the predicted active compound A64 was chemically synthesized, structure elucidated by NMR and in vivo tested in multidrug resistant strain of Plasmodium yoelii nigeriensis infected mice. The experimental results obtained agreed well with the predicted values.

Pharmacophore, QSAR, and ADME based semisynthesis and in vitro evaluation of ursolic acid analogs for anticancer activity

In the present work, QSAR models for predicting the activities of ursolic acid analogs against human lung (A-549) and CNS (SF-295) cancer cell lines were developed by a forward stepwise multiple linear regression method using a leave-one-out approach. The regression coefficient (r(2)) and the cross-validation regression coefficient (rCV(2)) of the QSAR model for cytotoxic activity against the human lung cancer cell line (A-549) were 0.85 and 0.80, respectively. The QSAR study indicated that the LUMO energy, ring count, and solvent-accessible surface area were strongly correlated with anticancer activity. Similarly, the QSAR model for cytotoxic activity against the human CNS cancer cell line (SF-295) also showed a high correlation (r(2) = 0.99 and rCV(2) = 0.96), and indicated that dipole vector and solvent-accessible surface area were strongly correlated with activity. Ursolic acid analogs that were predicted to be active against these cancer cell lines by the QSAR models were semisynthesized and characterized on the basis of their (1)H and (13)C NMR spectroscopic data, and were then tested in vitro against the human lung (A-549) and CNS (SF-295) cancer cell lines. The experimental results obtained agreed well with the predicted values.

Rational Design of Novel Anti-microtubule Agent (9-Azido-Noscapine) from Quantitative Structure Activity Relationship (QSAR) Evaluation of Noscapinoids

An anticough medicine, noscapine [(S)-3-((R)4-methoxy-6-methyl-5,6,7,8-tetrahydro-[1,3]dioxolo[4,5-g]isoquinolin-5-yl)-6,7-dimethoxyiso-benzofuran-1(3H)-one], was discovered in the authors' laboratory as a novel type of tubulin-binding agent that mitigates polymerization dynamics of microtubule polymers without changing overall subunit-polymer equilibrium. To obtain systematic insight into the relationship between the structural framework of noscapine scaffold and its antitumor activity, the authors synthesized strategic derivatives (including two new ones in this article). The IC(50) values of these analogs vary from 1.2 to 56.0 µM in human acute lymphoblastic leukemia cells (CEM). Geometrical optimization was performed using semiempirical quantum chemical calculations at the 3-21G* level. Structures were in agreement with nuclear magnetic resonance analysis of molecular flexibility in solution and crystal structures. A genetic function approximation algorithm of variable selection was used to generate the quantitative structure activity relationship (QSAR) model. The robustness of the QSAR model (R(2) = 0.942) was analyzed by values of the internal cross-validated regression coefficient (R(2) (LOO) = 0.815) for the training set and determination coefficient (R(2) (test) = 0.817) for the test set. Validation was achieved by rational design of further novel and potent antitumor noscapinoid, 9-azido-noscapine, and reduced 9-azido-noscapine. The experimentally determined value of pIC(50) for both the compounds (5.585 M) turned out to be very close to predicted pIC(50) (5.731 and 5.710 M).

QSAR studies on the depuration rates of polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers and polychlorinated biphenyls in mussels (Elliptio complanata)

Based on the mechanism of action, a quantitative structure–activity relationship (QSAR) model for the depuration rate constants (k d) of 28 PAHs, 8 PBDEs and 28 PCBs in mussels (Elliptio complanata) was constructed by partial least squares (PLS) regression, following the guidelines for development and validation of QSAR models. For the training set of the QSAR model, r 2 = 0.953, the cross-validated regression coefficient () was 0.947. The predicted log k d values for the validation set were consistent with the observed values, with a standard error (SE) of 0.160 log units and a squared correlation coefficient () of 0.892. Comparatively, the developed QSAR model had good robustness, predictive ability and extended applicability domain. The electrophilicity index (ω), molecular polarizability (α), the averages of the negative potentials on the molecular surface () and the balance parameter of surface potential (τ) were the key parameters governing the log k d values in the QSAR model, which indicated that the log k d value was mainly related to the partition ability, electrostatic interactions, and van der Waals interactions of compounds.

Detection of Trace Melamine in Raw Materials Used for Protein Pharmaceutical Manufacturing Using Surface-Enhanced Raman Spectroscopy (SERS) with Gold Nanoparticles

Melamine, a nitrogen-rich molecule, was found as an adulterant in pet foods in 2007 in North America and in milk products in 2008 in China. These scandalous abuses of melamine have alarmed the biopharmaceutical industry and the FDA and alerted them to potential adulteration and contamination of melamine in raw materials used to make protein therapeutics. Highly sensitive analytical methods are needed to screen melamine adulteration and contamination in raw materials. We conducted surface-enhanced Raman spectroscopy (SERS) experiments to test trace melamine spiked in three raw materials commonly used for protein pharmaceutical formulation and purification, including sucrose, urea, and arginine, with a portable Raman device and gold nanoparticles. The detection limit of 10 ppb in raw material dissolved in 30:70% water/acetonitrile is equivalent to 0.5 ppm in solid raw material. It has excellent linearity in the concentration range measured. The cross-validation regression coefficient R(2) and the standard error of prediction (SEP) are 0.960 and 7.18 ppb, respectively, in sucrose. The R(2) and SEP are 0.958 and 9.15 ppb in urea. It has a relatively lower R(2) = 0.630 and a SEP of 35.0 ppb in arginine, which could be due to the competitive adsorption of arginine molecules to the surfaces of gold nanoparticles. The detection of melamine using the SERS technique is rapid (within 3 minutes), convenient, and requires no extraction procedure, offering an alternative method for screening melamine in raw materials at biopharmaceutical manufacture sites.