The chemical characterization analysis of a medical device often results in chemical substances with unknown toxicities. While identification of each individual toxicity could result in a time-consuming hurdle with tremendous labor and financial burden, quantitative structure-activity relationship (QSAR) is of great significance for toxicity risk assessment of such chemical substances. By establishing quantitative relationship between the molecular structures or active groups of similar chemical compounds with their biological activities, QSAR can be utilized to predict the toxicity of such target compounds with significantly reduced cost and time. In this article, the authors generally summarized the mechanisms of QSAR approaches, current applications of QSAR modeling in the field of medical device, an introduction of the characteristics of publicly and commercially-available QSAR software, and briefly explored future trends of QSAR modeling in medical device toxicological risk assessment. The utilization of QSAR would undoubtedly further advance the toxicological risk assessment of medical devices.
Quantitative Structure-Activity Relationship ; Risk Assessment ; Software

Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.
Amifostine ; Computer Simulation ; Cytostatic Agents ; Drug Discovery ; Drug Therapy ; Humans ; Quantitative Structure-Activity Relationship ; Radiation Tolerance ; Radiotherapy ; United States Food and Drug Administration

The human pregnane X receptor (hPXR) plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards hPXR. Heuristic method (HM)-Best Subset Modeling (BSM) and HM-Polynomial Neural Networks (PNN) were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain (AD) of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved (for HM-BSM, r (2)=0.881, q LOO (2) =0.797, q EXT (2) =0.674; for HM-PNN, r (2)=0.882, q LOO (2) =0.856, q EXT (2) =0.655). The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to hPXR.
Computer Simulation ; Humans ; Models, Statistical ; Molecular Weight ; Neural Networks (Computer) ; Quantitative Structure-Activity Relationship ; Receptors, Steroid ; agonists ; chemistry ; Small Molecule Libraries ; chemistry ; Static Electricity

OBJECTIVES: For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. METHODS: There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. RESULTS: We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. CONCLUSIONS: We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used with predicted toxicity results. Furthermore, by presenting the suitability of individual predicted results, we aimed to provide a foundation that could be used in actual assessments and regulations.
Humans ; Quantitative Structure-Activity Relationship* ; Reproducibility of Results ; Social Control, Formal

The shape and structure of granules are controlled by the granulation process, which is one of the main factors to determine the nature of the solid dosage forms. In this article, three kinds of granules of a traditional Chinese medicine for improving appetite and promoting digestion, namely, Jianwei Granules, were prepared using granulation technologies as pendular granulation, high speed stirring granulation, and fluidized bed granulation and the powder properties of them were investigated. Meanwhile, synchrotron radiation X-ray computed micro tomography (SR-µCT) was applied to quantitatively determine the irregular internal structures of the granules. The three-dimensional (3D) structure models were obtained by 3D reconstruction, which were more accurately to characterize the three-dimensional structures of the particles through the quantitative data. The models were also used to quantitatively compare the structural differences of granules prepared by different granulation processes with the same formula, so as to characterize how the production process plays a role in the pharmaceutical behaviors of the granules. To focus on the irregularity of the particle structure, the box counting method was used to calculate the fractal dimensions of the granules. The results showed that the fractal dimension is more sensitive to reflect the minor differences in the structure features than the conventional parameters, and capable to specifically distinct granules in structure. It is proved that the fractal dimension could quantitatively characterize the structural information of irregular granules. It is the first time suggested by our research that the fractal dimension difference (Df,c) between two fractal dimension parameters, namely, the volume matrix fractal dimension and the surface matrix fractal dimension, is a new index to characterize granules with irregular structures and evaluate the effects of production processes on the structures of granules as a new indicator for the granulating process control and optimization.
Drugs, Chinese Herbal ; analysis ; Fractals ; Medicine, Chinese Traditional ; Powders ; Quantitative Structure-Activity Relationship ; Synchrotrons ; Technology, Pharmaceutical ; Tomography, X-Ray Computed

The beta-secretase is one of prospective targets against Alzheimer's disease (AD). A three-dimensional quan titative structure-activity relationship (3D-QSAR) model of Hydroethylamines (HEAs) as beta-secretase inhibitors was established using Topomer CoMFA. The multiple correlation coefficient of fitting, cross validation and external validation were r2 = 0.928, q(loo)2 = 0.605 and r(pred)2 = 0.626, respectively. The 3D-QSAR model was used to search R groups from ZINC database as the source of structural fragments. As a result, a series of R groups with relatively high activity contribution was obtained to design a total of 15 new compounds, with higher activity than that of the template molecule. The molecular docking was employed to study the interaction mode between the new compounds as ligands and beta-secretase as receptors, displaying that hydrogen bond and hydrophobicity played important roles in the binding affinity between the new compounds and beta-secretase. The results showed that Topomer CoMFA and To pomer Search could be effectively used to screen and design new molecules of HEAs as beta-secretase inhibitors, and the designed compounds could provide new candidates for drug design targeting AD.
Amyloid Precursor Protein Secretases ; antagonists & inhibitors ; Drug Design ; Hydrophobic and Hydrophilic Interactions ; Ligands ; Molecular Docking Simulation ; Quantitative Structure-Activity Relationship

The combined application of statins that inhibit HMG-CoA reductase and fibrates that activate PPAR-α can produce a better lipid-lowering effect than the simple application, but with stronger adverse reactions at the same time. In the treatment of hyperlipidemia, the combined administration of TCMs and HMG-CoA reductase inhibitor in treating hyperlipidemia shows stable efficacy and less adverse reactions, and provides a new option for the combined application of drugs. In this article, the pharmacophore technology was used to search chemical components of TCMs, trace their source herbs, and determine the potential common TCMs that could activate PPAR-α. Because there is no hyperlipidemia-related medication reference in modern TCM classics, to ensure the high safety and efficacy of all selected TCMs, we selected TCMs that are proved to be combined with statins in the World Traditional/Natural Medicine Patent Database, analyzed corresponding drugs in pharmacophore results based on that, and finally obtained common TCMs that can be applied in PPAR-α and combined with statins. Specifically, the pharmacophore model was based on eight receptor-ligand complexes of PPAR-α. The Receptor-Ligand Pharmacophore Generation module in the DS program was used to build the model, optimize with the Screen Library module, and get the best sub-pharmacophore, which consisted of two hydrogen bond acceptor, three hydrophobic groups and 19 excluded volumes, with the identification effectiveness index value N of 2. 82 and the comprehensive evaluation index CAI value of 1. 84. The model was used to screen the TCMD database, hit 5,235 kinds of chemical components and 1 193 natural animals and plants, and finally determine 62 TCMs. Through patent retrieval, we found 38 TCMs; After comparing with the virtual screening results, we finally got seven TCMs.
Acyl Coenzyme A ; metabolism ; Animals ; Databases, Factual ; Drugs, Chinese Herbal ; chemistry ; pharmacology ; Hydrophobic and Hydrophilic Interactions ; Hydroxymethylglutaryl-CoA Reductase Inhibitors ; chemistry ; pharmacology ; Lipid Metabolism ; drug effects ; Lipids ; blood ; Medicine, Chinese Traditional ; Models, Molecular ; Quantitative Structure-Activity Relationship ; Technology

To study the quantitative structure-activity relationship (QSAR) between the stuctures of 29 flavonoids and the inhibitory activity of their multidrug resistance-associated protein (MRP) 1 and 2 by using the comparative molecular similarity index analysis (CoMSIA). By studying the impact of the combination of different molecular force fields, researchers obtained the molecular force fields that played an important role in inhibiting the activity of MRP1 and MRP2, built the optimized QSAR model, and discussed the structural modification method for flavonoids' multidrug resistance-associated protein inhibitor. The results of the study could not only provide the guidance for new drug R&D, but also help partially discuss the synergy mechanism between MRP1 and MRP2 receptors and traditional Chinese medicines containing flavonoids.
Drugs, Chinese Herbal ; chemistry ; pharmacology ; Flavonoids ; chemistry ; pharmacology ; Humans ; Models, Molecular ; Multidrug Resistance-Associated Proteins ; antagonists & inhibitors ; chemistry ; Quantitative Structure-Activity Relationship

Today, the understanding of the sequence and structure of biologically relevant targets is growing rapidly and researchers from many disciplines, physics and computational science in particular, are making significant contributions to modern biology and drug discovery. However, it remains challenging to rationally design small molecular ligands with desired biological characteristics based on the structural information of the drug targets, which demands more accurate calculation of ligand binding free-energy. With the rapid advances in computer power and extensive efforts in algorithm development, physics-based computational chemistry approaches have played more important roles in structure-based drug design. Here we reviewed the newly developed computational chemistry methods in structure-based drug design as well as the elegant applications, including binding-site druggability assessment, large scale virtual screening of chemical database, and lead compound optimization. Importantly, here we address the current bottlenecks and propose practical solutions.
Computational Biology ; Drug Design ; Drug Discovery ; High-Throughput Screening Assays ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Quantitative Structure-Activity Relationship

CB2-selective agonists have drawn attention in drug discovery, since CB2 becomes a promising target for the treatment of neuropathic pain without psychoactive or other CNS-related side effects. However, the lack of experimental data of the 3D structures of human cannabinoid receptors hampers the understanding of the binding modes between ligands and CB2 by traditional methods. In the present work, combinational molecular modeling studies including flexible docking, MD simulations and free energy calculations were performed to investigate the interaction modes and mechanism of CB2-unselective agonist CP55940 and CB2-selective agonist GW842166X, separately binding with the homology model of CB2 in a DPPC/TIP3P simulated membrane environment. The binding free energies calculated by MM-PBSA method give an explanation for the activity differences of the studied ligands. Binding free energies decomposition by MM-GBSA method shows that the van der Waals interaction is the dominant driving force during the binding process. Our MD simulations demonstrate that Phe197 could be a critical residue for the binding of CB2-selective agonists. Furthermore, by using the MD simulated binding conformer as a template, the 3D-QSAR studies were performed with the comparative molecular field analysis (CoMFA) approach on a set of GW842166X analogues. A combinational exploration of both CoMFA steric and potential contour maps for CB2 affinities and the MD studied interaction modes sheds light on the structural requirements for CB2 agonists and serves as a basis for the design of novel CB2 agonists.
Binding Sites ; Cyclohexanols ; chemistry ; Humans ; Ligands ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Protein Binding ; Pyrans ; chemistry ; Pyrimidines ; chemistry ; Quantitative Structure-Activity Relationship ; Receptor, Cannabinoid, CB2 ; agonists ; chemistry