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A quantitative structure - toxicity relationship of drugs on rat. E-mail: ajouyban hotmail. A full house meaning in hotel structure toxicity relationship QSTR is proposed to correlate the toxicity of drugs on rat after intravenous administration.
The computational descriptors of drug molecules are calculated using HyperChem software and regressed against LD 50 of drugs collected from the literature. The best QSTR model is:. These drugs include busulfan, calcitriol, epinephrine, triaziquone etc. After excluding these data points, the model is:. Small changes in the model constants showed that what do toxic mean in a relationship model is robust and could be considered as a predictive model.
Because of various toxic mechanisms, high discrepancy in reported LD 50 of some drugs from different references, high APD value could be justified. As an example, LD 50 s of dixyrazine are The APD could be considered as acceptable over range if the experimental discrepancies between reported LD 50 from different laboratories are kept in mind. Para evaluar la precisión del modelo QSTR propuesto se utilizaron el coeficiente de correlación Rel valor F y el porcentaje de desviación promedio APD entre los valores calculados y experimentales de LD El mejor modelo QSTR es:.
Después de excluir estos puntos, el modelo obtenido es:. Con algunos pequeños cambios en las constantes el modelo demostró ser robusto y por lo tanto se consideró como altamente predictivo. El alto valor de APD obtenido podría justificarse gracias a los diferentes mecanismos de toxicidad y a la gran dispersión de los valores de LD 50 presentados en la literatura. The first step in the development of cause and effect quizlet new drug is the synthesis of a potential new drug molecule or extraction of a candidate chemical compound from natural sources.
Relattionship safety and efficacy of a drug have to be investigated before its marketing. These investigations start from in vitro studies and continue for most of the biologic effects of the molecule which should be characterized in animal studies before human drug trials. Human testing must then go forward in three conventional phases before the drug is considered for final approval of what do toxic mean in a relationship.
A fourth phase of data gathering follows after approval for general use. Enormous costs, from million to over million USD, are involved in the development of a successful new drug. These costs are spent for searching new molecules and a huge number of candidates may be synthesized for each successful drug, basic and clinical studies and promotion of the ultimate candidate molecule. It is primarily because of the economic investment and risks involved that most of the new drugs are now developed in the laboratories of pharmaceutical companies.
At the same time, the incentives to succeed in drug development are equally enormous 1. It is reltionship to recognize the limitations of preclinical testing. These include the following: A Toxicity testing is time-consuming and expensive. During the last decade, what do toxic mean in a relationship total cost of preclinical pharmacology and toxicology studies was estimated to be at least 41 million USD per a successful drug and years to collect and analyze data.
B Large numbers of animals are used to obtain preclinical data and there are limitations for animal studies in some societies. Scientists are properly concerned about this situation, and progress has been made toward reducing the number of animals required while still obtaining valid data. In vitro methods such as cell and tissue cultures are increasingly being what do toxic mean in a relationship, but their predictive capacity is still wbat limited. C Extrapolation of toxicity data from animals to humans is not completely reliable.
For any given compound, the total toxicity data from all species have a very high predictive value for it's toxicity in relationhsip. However, there are limitations on the extent of the available databases. D For statistical reasons, rare adverse effects are unlikely to be detected, just as in clinical trials 2. Finding an active compound with maximal pharmacologic effects and minimal adverse and toxic effects is the most important goal of drug development investigations.
Different mechanisms what do toxic mean in a relationship involved in the toxicity of a compound and the tools on understanding such a complex phenomena are still very limited. The severe adverse and toxic effects result in decreasing the chance of further progress of introduction of the new drugs. In addition to the experimental investigations, the computational methods relarionship also be employed in order what do toxic mean in a relationship determining the safety and efficacy of the new candidates.
By considering that drugs toxic effects result from x physico-chemical reactions with biological systems, basically, there must exist a relationship between physico-chemical characteristics of drugs and their toxic effects. This relationship could be determined for the kinetic or dynamic effects of physicochemical properties of drugs. Statistical analyses of drug data, for correlating toxicity with chemical structure and proposing mathematical models with ability of toxicity prediction, makes the drug discovery investigations faster and more cost-effective.
These models could be applied for predicting toxicity of new compounds based on their structures 3. Quantitative structure - activity QSARs and quantitative structure - toxicity QSTRs relationships have provided a valuable approach in research into the toxicity of organic chemicals in such studies 4. The presence of shat implies that factors or specific mechanisms other than log P are involved in their cytotoxicity. Therefore, they obtained the two-parameter QSTR equation in order to describe the correlation between the love breakup motivational quotes of the various catechols, their lipophilicity log P and their degree of ionization pK a1 towards isolated rat hepatocytes:.
Moridani et al. QSTR models can be used as a powerful tool for understanding the toxic mechanisms that may be common among a group of similar compounds such as catechols. This is possible by first deriving the QSTR equations correlating different physico-chemical parameters to catechol toxicity. Then these QSTR what does not metered connection mean can be applied to other biological endpoints that contribute to the toxic mechanisms; i.
This information would be valuable to a research group that uses catechol functional groups for drug design and the optimization of a relationehip molecule for a desired physico-chemical characteristic 5. In another investigation on LD of some nean in human subjects, the following relation was reported. LD data of drugs were collected by measuring blood concentration of drugs obtained from txic suicides victims. In Eq. To further improve Eq.
Drugs with Cl and Br substitutions fit well with this equation. In two recent models log P is one of the parameters that is determining the absorption of drugs from gastrointestinal tract and also drug penetration to the CNS 6. The aim of the present study is to provide a model for prediction of drug toxicity based on acute toxicity data LD 50 what do toxic mean in a relationship rats and extend this model for toxicity evaluation of new drug relstionship.
Details of the ij data including the drug name, LD 50 values and the calculated descriptors are listed in Table 1. LD 50 was used as dependent variable and other descriptors as independent variables and the best equation with consideration of correlation coefficient Rpercentage deviation PDF value and average percentage deviation APD was developed. All calculations were carried out in SPSS 11 environment using the written programs through this project.
Preliminary analysis was carried out employing all independent variables by backward regression command of SPSS software. The resulting equation was:. The above equation with applying experimental data of different drugs was resulted. APD for some data points for Eq. After excluding the outlier data points, the resulting equation was:. For providing practical application of Eq. Based on this classification, drugs are divided into six groups from highly toxic to harmless Table 3 shows the APD values for different groups and number of data what do toxic mean in a relationship each group.
Considering Table 3with increasing toxicity of the drugs, predictive ability of the model what does linear regression equation tell you decreased. From statistical viewpoint, this limitation results from two factors: A Number of data in what do toxic mean in a relationship toxic group is very small and B Relationsihp of very small numerical values of high toxic drugs on the total What do toxic mean in a relationship 50 mean is negligible and lead to the reduced accuracy of the equation.
Any error in measuring or reporting the experimental toxicity data is another source of prediction error. In some cases, the numerical difference of the reported LD 50 for a single drug, on a given animal and a common administration route is about folds. For example, LD 50 of benzyl benzoate in rat after oral administration is 1. Table 4 shows some of different data for LD 50 of drugs in rat after intravenous administration. As what is phylogenetic in a sentence conclusion, a quantitative structure toxicity relationship QSTR was proposed to correlate toxicity of drugs relationxhip rat.
The proposed model is robust and dl be considered as a predictive model. Because of various toxic mechanisms, high discrepancy in reported LD 50 of some of drugs from different references, high APD value could be justified. The APD could be considered as acceptable error range if the experimental discrepancies between reported LD 50 from what do toxic mean in a relationship laboratories are kept in mind. Billstein, How the pharmaceutical industry brings an antibiotic drug to market in the United States, Antimicrob.
Agents Chemother. Katzung, "Basic and Clinical Pharmacology. J Feng, L. Lurati, H. Ouyang, T. Robinson, Y. Wang, S. Yuan and S. Young, Predictive toxicology: Benchmarking molecular descriptors and statistical method, J. Khadikar, K. Mather, S. Singh, A. Phadnis, A. Shrivastava and M. Moridani, A. Siraki, T. Chevaldina, H. Scobie ln P. Hansch and A.