Prediction Of Oral Absorption Research Articles

The effects of degree and duration of supersaturation on in vivo absorption profiles for highly permeable drugs, dipyridamole and ketoconazole

The prediction of oral absorption from a supersaturating drug delivery system (SDDS) remains a significant challenge. Here we evaluated the effects of the degree and duration of supersaturation on in vivoabsorption for dipyridamole and ketoconazole. Various dose concentrations of supersaturated suspensions were prepared by a pH shift method, and in vitro dissolution and in vivo absorption profiles were determined. For dipyridamole, the duration of supersaturation decreased with the increase of the dose concentration owing to rapid precipitation. For ketoconazole, the initially constant dissolved concentrations due probably to the liquid–liquid phase separation (LLPS) as a reservoir were observed at high dose concentrations. However, the LLPS did not delay the peak plasma concentration of ketoconazole in rats, indicating that drug molecules were immediately released from the oil phase to the bulk aqueous phase. For both model drugs, the degree of supersaturation, but not the duration of supersaturation, correlated with systemic exposure, indicating quick drug absorption before precipitation. Therefore, the degree of supersaturation is an important parameter compared with the duration of supersaturation for enhancing the in vivo absorption of highly permeable drugs. These findings would help develop a promising SDDS.

Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption.

Oral absorption of compounds depends on many physiological, physiochemical and formulation factors. Two important properties that govern oral absorption are in vitro permeability and solubility, which are commonly used as indicators of human intestinal absorption. Despite this, the nature and exact characteristics of the relationship between these parameters are not well understood. In this study a large dataset of human intestinal absorption was collated along with in vitro permeability, aqueous solubility, melting point, and maximum dose for the same compounds. The dataset allowed a permeability threshold to be established objectively to predict high or low intestinal absorption. Using this permeability threshold, classification decision trees incorporating a solubility-related parameter such as experimental or predicted solubility, or the melting point based absorption potential (MPbAP), along with structural molecular descriptors were developed and validated to predict oral absorption class. The decision trees were able to determine the individual roles of permeability and solubility in oral absorption process. Poorly permeable compounds with high solubility show low intestinal absorption, whereas poorly water soluble compounds with high or low permeability may have high intestinal absorption provided that they have certain molecular characteristics such as a small polar surface or specific topology.

Hypnotic Profile of Imines from Benzimidazole Chalcones: Mechanism of Synthesis, DFT Studies and in silico Screening

A series of 1-(1H-benzimidazol-2-yl)-3-substituted phenylprop-2-en-1-ylidene] amino}-1,3,4-thiadiazole-2- thiols (6a-6f) were synthesized by the acid catalyzed nucleophilic addition reaction between 1-(1H-benzimidazol-2-yl)-3- phenylprop-2-en-1-ones (4a-4f) and 5-amino-1,3,4-thiadiazole-2-thiol. All the synthesized compounds were characterised by IR, (1)HNMR, (13)CNMR, Mass and elemental analyses. A transition state calculation obtained from DFT study to explore the molecular mechanism of action of the synthetic route. The mechanism of synthesis revealed that the imidazole system can make an increase in the electrophilic character of carbonyl carbon in the benzimidazole chalcones. So the electron deficient carbonyl carbon could be efficiently attacked on the amino group of 1,3,4-thiadiazole ring to forms an imine linkage between the two heterocyclic systems. All the titled derivatives at a dose level of 10mg/kg body weight potentiate the hypnotic action of Phenobarbitone (at a dose of 10mg/kg body weight i.p.). The compounds such as 6b, 6a, and 6c showed a significant percentage increase in sleeping time relative to the control experiment 423.8, 387.6 and 329.5 respectively. The preclinical evaluation of the compounds was ascertained by blood-brain barrier, human oral absorption prediction and in silico toxicity assessment.

Effect of Acid-Base Property in Conjunction with Molecular Descriptors for Chemical Drugs and Bioactive Ingredients of Traditional Chinese Medicine on Prediction of Oral Absorption by Biopartioning Micellar Chromatography

Effect of Acid-Base Property in Conjunction with Molecular Descriptors for Chemical Drugs and Bioactive Ingredients of Traditional Chinese Medicine on Prediction of Oral Absorption by Biopartioning Micellar Chromatography

From preclinical to human – prediction of oral absorption and drug–drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example

A case example is presented in which the physiologically based modeling approach has been used to model the absorption of a lipophilic BCS Class II compound predominantly metabolized by CYP3A4, and to assess the interplay of absorption related parameters with the drug-drug interaction (DDI) potential. The PBPK model was built in the rat using Gastroplus® to study the absorption characteristics of the compound. Subsequently relevant model parameters were used to predict the non-linear human PK observed during first-in-human study after optimizing the absorption model for colonic absorption, bile micelle solubilization and unbound fraction in gut enterocytes (fu(gut)) using SIMCYP® simulator. The model fitted absorption parameters were then used to assess the drug-drug interaction (DDI) potential of the test compound when administered along with multiple doses of a potent CYP 3A4 inhibitor, ketoconazole. The impact of fu(gut) in the extent of DDI was assessed using parameter sensitivity analysis. After optimizing the preclinical model and taking into consideration bile micelle solubilization and colonic absorption, the non-linear pharmacokinetics of the test compound was satisfactorily predicted in man. Sensitivity analysis performed with the absorption parameter fu(gut) indicated that it could be an important parameter in predicting oral absorption. In addition, DDI simulations using SIMCYP® suggest that C(max) and AUC ratios may also be sensitive to the fu(gut) input in the model. Since fu(gut) cannot be measured experimentally, sensitivity analysis may help in assessing the importance of fu(gut) in human PK and DDI prediction using SIMCYP®.

Measurement and Prediction of Oral Absorption

After defining oral absorption, this review article summarizes the influence of different physiological and physicochemical parameters on oral absorption explained according to the situation at BayerScheringPharma. Along the optimization process in industries, prediction of oral absorption is exemplified. In silico prediction of oral absorption in early stages of research is highlighted with three examples, i.e., a classification algorithm, a single parameter prediction, and a linear free-energy relationship. In vitro prediction of permeation using Caco-2 cell layers and physicochemical modeling is exemplified. The influence of solubility is shown by a concrete example, in particular, using physiologically based pharmacokinetic (PBPK) modeling and physicochemical parameters. For later stages, optimization examples of in vivo animal models stress the importance of excellent in vivo data and in vivo/in vivo correlations for the prediction of human oral absorption from animal experiments.

Prediction of oral absorption in humans by experimental immobilized artificial membrane chromatography indices and physicochemical descriptors

The purpose of the present study was to examine the human oral absorption (HOA) predictability of the experimentally determined immobilized artificial membrane (IAM) chromatography capacity factor ( log k ′ IAM ) in conjunction with physicochemical descriptors. Transcellular permeation was modeled based on determination of log k ′ IAM considering pH partition hypothesis, and the independent variables were polar surface area (PSA) and molecular weight (MW). The correlation between log k ′ IAM determined at different pH and n-octanol/water partition coefficient (log P) and contribution of polarity (PSA) and size (MW) in the transcellular permeation model were the extension to the previous work. A data set of 37 compounds with partition coefficient values taken from the literature was employed to show importance of ionic interaction in oral absorption prediction. The highest log k ′ IAM value among screened pH 4.5, 5.5, 6.5 and 7.4 ( log k ′ IAM 4.5 − 7.4 ) in conjunction with PSA predicted HOA with coefficient of determination (CD) of 0.9001 compare to log k ′ IAM 4.5 − 7.4 alone with CD of 0.8454 after excluding bretylium from the set of 28 structurally diverse drugs for known reason. PSA helped to avoid over estimation of HOA for amiloride, famotidine and furosemide. The model was tested for its applicability in drug development program and found to predict oral absorption using physically meaningful and structurally related properties making them relatively straightforward for a medicinal chemist to interpret.

ADME Evaluation in Drug Discovery. Part 7. Prediction of Oral Absorption by Correlation and Classification.

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ADME Evaluation in Drug Discovery. 7. Prediction of Oral Absorption by Correlation and Classification

A critically evaluated database of human intestinal absorption for 648 chemical compounds is reported in this study, among which 579 are believed to be transported by passive diffusion. The correlation analysis between the intestinal absorption and several important molecular properties demonstrated that no single molecular property could be used as a good discriminator to efficiently distinguish the poorly absorbed compounds from those that are well absorbed. The theoretical correlation models for a training set of 455 compounds were proposed by using the genetic function approximation technique. The best prediction model contains four molecular descriptors: topological polar surface area, the predicted distribution coefficient at pH = 6.5, the number of violations of the Lipinski's rule-of-five, and the square of the number of hydrogen-bond donors. The model was able to predict the fractional absorption with an r = 0.84 and a prediction error (absolute mean error) of 11.2% for the training set. Moreover, it achieves an r = 0.90 and a prediction error of 7.8% for a 98-compound test set. The recursive partitioning technique was applied to find the simple hierarchical rules to classify the compounds into poor (%FA < or = 30%) and good (%FA > 30%) intestinal absorption classes. The high quality of the classification model was validated by the satisfactory predictions on the training set (correctly identifying 95.9% of the compounds in the poor-absorption class and 96.1% of the compounds in the good-absorption class) and on the test set (correctly identifying 100% of the compounds in the poor-absorption class and 96.8% of the compounds in the good-absorption class). We expect that, in the future, the rules for the prediction of carrier-mediated transporting and first pass metabolism can be integrated into the current hierarchical rules, and the classification model may become more powerful in the prediction of intestinal absorption or even human bioavailability. The databases of human intestinal absorption reported here are available for download from the supporting Web site: http://modem.ucsd.edu/adme.