scholarly journals Model-Based Analysis of Biopharmaceutic Experiments To Improve Mechanistic Oral Absorption Modeling: An Integrated in Vitro in Vivo Extrapolation Perspective Using Ketoconazole as a Model Drug

2017 ◽  
Vol 14 (12) ◽  
pp. 4305-4320 ◽  
Author(s):  
Shriram M. Pathak ◽  
Aaron Ruff ◽  
Edmund S. Kostewicz ◽  
Nikunjkumar Patel ◽  
David B. Turner ◽  
...  
2020 ◽  
Vol 21 (10) ◽  
pp. 746-750
Author(s):  
Jaydeep Sinha ◽  
Stephen B. Duffull ◽  
Bruce Green ◽  
Hesham S. Al-Sallami

Background : In vitro-in vivo extrapolation (IVIVE) of hepatic drug clearance (>CL) involves the scaling of hepatic intrinsic clearance (CLint,uH) by functional liver size, which is approximated by total liver volume (LV) as per the convention. However, in most overweight and obese patients, LV includes abnormal liver fat, which is not thought to contribute to drug elimination, thus overestimating drug CL. Therefore, lean liver volume (LLV) might be a more appropriate scaler of CLint,uH. Objective : The objective of this work was to assess the application of LLV in CL extrapolation in overweight and obese patients (BMI >25 kg/m2) using a model drug antipyrine. Methods: Recently, a model to predict LLV from patient sex, weight, and height was developed and evaluated. In order to assess the LLV model’s use in IVIVE, a correlation-based analysis was conducted using antipyrine as an example drug. Results : In the overweight group (BMI >25 kg/m2), LLV could describe 36% of the variation in antipyrine CL (R2 = 0.36), which was >2-fold higher than that was explained by LV (R2 = 0.17). In the normal-weight groupjats:(BMI ≤25 kg/m2), the coefficients of determination were 58% (R2 = 0.58) and 43% (R2= 0.43) for LLV and LV, respectively. Conclusion : The analysis indicates that LLV is potentially a more appropriate descriptor of functional liver size than LV, particularly in overweight individuals. Therefore, LLV has a potential application in IVIVE of CL in obesity.


2021 ◽  
Vol 34 (4) ◽  
pp. 1175-1182
Author(s):  
Luise Henneberger ◽  
Julia Huchthausen ◽  
Niklas Wojtysiak ◽  
Beate I. Escher

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.


2014 ◽  
Vol 2 (4) ◽  
pp. 63-70 ◽  
Author(s):  
Danyel Jennen ◽  
Jan Polman ◽  
Mark Bessem ◽  
Maarten Coonen ◽  
Joost van Delft ◽  
...  

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