Perception, Planning, and Scoping, Problem Formulation, and Hazard Identification
Ted W. Simon in Environmental Risk Assessment, 2019
Of necessity, QSAR requires a prediction model for the biological activity. Often, this is a statistical regression of the predicted value versus the predictor value.182 One of the early uses of QSAR was EPA’s attempt to predict dermal permeability of chemicals from water. Measurements of the dermal permeability coefficient Kp were available for 90 chemicals, and EPA used a regression model to estimate Kp for other chemicals. The independent variables were the octanol-water partition coefficient and the molecular weight. Unfortunately, this regression method did not work for high molecular weight highly lipophilic chemicals, and an effective prediction domain was established inside which the regression was applicable.183
Prediction of Human Percutaneous Absorption With Physicochemical Data
Rhoda G. M. Wang, James B. Knaak, Howard I. Maibach in Health Risk Assessment, 2017
During skin exposure studies, it would be useful for scientists to be able to estimate the skin absorption of a chemical. Numerous studies have examined the relationship of various properties of chemicals that might aid in this endeavor. Reasonable correlations have been obtained between percutaneous absorption and lipid/water partition coefficients for certain homologous series of compounds: phenols,1 alcohols,2 steroids,3 and hair dyes.4 In these studies, percutaneous absorption was expressed in terms of a permeability constant (Kp) measured from an aqueous vehicle. In this way, one could extrapolate between doses, because a Kp value by definition is the flux normalized for concentration. The octanol/water partition coefficient (Kow) is now most commonly used as a measure of lipophilicity, because numerous published Kow values are available and because methods are available for Kow estimations based on chemical structure.
Skin Absorption Databases and Predictive Equations
Richard H. Guy, Jonathan Hadgraft in Transdermal Drug Delivery, 2002
Equation (4) is the basis of several equations for estimating SC permeability coefficients [e.g., (14)]. In these it is assumed that the SC–water partition coefficient is related to the octanol–water partition coefficient through a power function of the general form in which the parameter b accounts for differences in lipophilic character of the SC lipids compared to octanol (14). The SC–water partition coefficient data in Chapter 4 indicate that the intercept (i.e., log â) predicted by Eq. (10) is small. The diffusion of small molecules in rubbery polymers is generally considered to be an activated process that varies exponentially with the size of the penetrant (15): where D0 is the diffusion coefficient of a hypothetical molecule having zero molecular volume (MV), and is a constant. Equations (10) and (11) can then be combined as indicated by Eq. (4). Potts and Guy (14) showed no significant degradation in predictive power when MV was replaced by MW (i.e., Dc = D0 exp(−γ1 MW)) and recommended the following functional form: in which a = log â + log(D0/Lc) and d1, = γ1 log e = 0.424 γ1. However, their data set consisted primarily of hydrocarbons. One would expect that MV would be better than MW for describing a chemically more heterogeneous data set (e.g., including halogenated hydrocarbons). Analysis of skin permeability measurements with Eq. (12) will provide values for a, b, and d1 that have attributable physicochemical meaning.
Preparation of an isorhamnetin phospholipid complex for improving solubility and anti-hyperuricemia activity
Published in Pharmaceutical Development and Technology, 2022
Fengmao Zou, Honghui Zhao, Aijinxiu Ma, Danni Song, Xiangrong Zhang, Xu Zhao
The distilled water was added to an equal volume of 1-octanol. The mixed solution was shaken in the air bath thermostatic oscillator at 37 °C for 24 h. The mixture was left to stand for 12 h and then the two phases were collected in the 100 ml conical flask. Next, an excess of ISO or ISO-PC was added to 10 ml of the 1-octanol phase. After being sonicated for 5 min, the solution was centrifuged at 4000 r/min for 15 min. The concentration of the 1-octanol phase (C1) was determined by the UV spectrophotometer. About 1 ml of the above 1-octanol phase was mixed with 1 ml of the aqueous phase and shaken at 37 °C for 24 h to equilibrate the ISO or ISO-PC between the two phases. The two phases were separated by centrifugation, and the aqueous phase was taken to determine the concentration (C2). The octanol-water partition coefficient (P) was calculated by the formula as follows: P = (C1-C2)/C2.
Molecular docking studies, anti-Alzheimer’s disease, antidiabetic, and anti-acute myeloid leukemia potentials of narcissoside
Published in Archives of Physiology and Biochemistry, 2023
Tingting Liu, Lixia Cao, Tingting Zhang, Huan Fu
After comparing the biological activity of narcissoside molecule against enzymes, ADME/T analysis was conducted to theoretically predict the effects and reactions of narcissoside molecule on human metabolism. As a result of this theoretical analysis, many parameters were obtained and these parameters are given in Table 5. The first parameter among these parameters is Solute Molecular Weight, which requires the molecule to have a certain molecular weight. Another parameter is Solute Total SASA, which is the total solvent accessible surface area (SASA) using a 1.4 Å radius probe. Another parameter is QP log p for octanol/water, which is the predicted octanol/water partition coefficient. Another important parameter is QPlogHERG, which is the numerical value of the estimated IC50 value when the HERG K channels are blocked. The next parameter is QPPCaco, which is Caco-2 cell permeability in the gut–blood barrier for inactive transport. Another parameter is QPlogBB, which is the coefficient of the brain–blood barrier of an orally taken drug. The next parameter is #metab, which is the number of Possible metabolic reactions for the afzelin molecule (Demir et al.2020, Kısa et al.2020, Taslimi et al.2020a, 2020b, Türkan et al.2020).
The impact of exposure route for class-based compounds: a comparative approach of lethal toxicity data in rodent models
Published in Drug and Chemical Toxicology, 2018
Yu Wang, Shuo Wang, Xiao N. Feng, Li C. Yan, Shan S. Zheng, Yue Wang, Yuan H. Zhao
Table 2 shows that absorption is the step that determines the toxicity of some classified compounds with different toxicity values. On the other hand, distribution is the step that determines the toxicity for other classified compounds with similar toxicity values. It is suggested that the rate determining step of a compound for toxicity is closely related to its physicochemical properties, leading to different or similar toxic effects from different routes. To investigate the effect of physicochemical properties on the rate determining step, stepwise analysis was used to add the molecular descriptors into the correlation models of toxicity from different routes. Table 3 lists the regression equations for the toxicities from i.v. route against i.p., s.c. and i.g. routes, respectively (Models 1, 2 and 3 in Table 3). Stepwise regression shows that inclusion of molecular volume (V), dipolarity/polarizability (S), hydrogen bond basicity (B) and octanol/water partition coefficient (log KOW) can improve the correlations for the toxicities from i.v. route against i.p., s.c. and i.g. routes, respectively (Models 4, 5 and 6 in Table 3). Although the correlations were not improved significantly, valuable information is still gathered from the equations.
Related Knowledge Centers
- Partition Coefficient
- 1-Octanol
- Dissociation
- Persistent Organic Pollutant
- Bioaccumulation
- Lipinski'S Rule of Five
- Toxicology
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- Cell
- Biological Membrane