Dear Fabio,

The following publications may be helpful:

1-Int J Toxicol. 2012 Jan-Feb;31(1):25-33. doi: 10.1177/1091581811429865. Epub 2012 Jan 3.

Correlation between in silico physicochemical characteristics of drugs and their mean residence time in human and dog.

Grabowski T1, Jaroszewski JJ, Gad SC, Feder M.

Author information

Abstract

The correlation between 52 physicochemical parameters and mean residence time (MRT) for 27 drugs used in human and dog were investigated. The physicochemical parameter values calculated provided a basis for deriving a series of arithmetic expressions, which were used to build a mathematical model describing the relationship between them and the MRT values. From the entire set of analyzed parameters, a subset of 14 was identified that contributed to the derivation of an arithmetic expression: Log(PSA - WPSA + ACID) x [XlogP - (LogKp - EAxLn(Caco2 + AMINE + SAF))] + (AMIDE + IP - FG) - Ln(MW + PISA) the value of which is highly correlated with the MRT value in dogs (P < .001) and allowed prediction of the MRT predicted (MRT(pred)). In humans, no correlation was found that allowed the calculation of MRT(pred). These results indicate that predicting the pharmacokinetics of any specific drug for humans based on pharmacokinetic data obtained in the dog should be undertaken with knowledge of the inherent limitations.

https://www.researchgate.net/publication/51979299_Correlation_Between_In_Silico_Physicochemical_Characteristics_of_Drugs_and_Their_Mean_Residence_Time_in_Human_and_Dog

http://www.ncbi.nlm.nih.gov/pubmed/22215410

2- In Silico Model as a Tool for Interpretation of Intestinal Infection Studies▿

Peter de Jong1,*,

Marc M. M. Vissers2,

Roelof van der Meer2,3 and

Ingeborg M. J. Bovee-Oudenhoven2,3

+Author Affiliations

1Department of Processing

2Department of Health and Safety, NIZO food research, Ede, The Netherlands

3Wageningen Centre for Food Sciences, Wageningen, The Netherlands

Next Section

ABSTRACT

In nutrition research the number of human in vivo experiments is limited because of the many restrictions and the high costs of testing in humans. Up to now predictive computer models aiming to enhance research have been rare or too complex, with many nonmeasurable adjustable parameters. This study aimed to develop a basic physicochemical computer model for a first quantitative interpretation of results obtained from in vivo intestinal experiments with bacteria. This new modeling approach is validated with results obtained from gut infection studies in vivo. The design of the model is described, and its ability to reproduce experimental data is evaluated. The model predictions are compared with new experimental data. The phenomena that take place in the gastrointestinal tract are summarized by model constants for growth, adherence, and release of bacteria. Although the model is far from describing all details and many processes in the intestine are combined, the model calculation results lead to reasonable conclusions and interesting hypotheses. One of these hypotheses concluded from the model outcomes is that Escherichia coli bacteria have a much lower intestinal growth rate in humans than in rats. Extra laboratory validation experiments proved the reliability of this hypothesis predicted by the model. In addition, the known protective effect of dietary calcium and detrimental effect of clindamycin on the growth and adherence of Salmonella bacteria could be quantified. From these results it is clear that the model enhances the interpretation of in vivo gastrointestinal experiments and will facilitate research trajectories towards new functional foods that improve resistance to pathogenic bacteria in humans.

Accepted manuscript posted online 22 November 2006, doi: 10.1128/AEM.01299-06

Appl. Environ. Microbiol. January 2007 vol. 73 no. 2 508-515

http://aem.asm.org/content/73/2/508.full

3- In silico labeling reveals the time-dependent label half-life and transit-time in dynamical systems

Thomas Maiwald†Email author,

Julie Blumberg†,

Andreas Raue,

Stefan Hengl,

Marcel Schilling,

Sherwin KB Sy,

Verena Becker,

Ursula Klingmüller and

Jens Timmer

†Contributed equally

BMC Systems Biology20126:13

DOI: 10.1186/1752-0509-6-13

©  Maiwald et al.; licensee BioMed Central Ltd. 2012

Received: 10 May 2011

Accepted: 27 February 2012

Published: 27 February 2012

Abstract

Background

Mathematical models of dynamical systems facilitate the computation of characteristic properties that are not accessible experimentally. In cell biology, two main properties of interest are (1) the time-period a protein is accessible to other molecules in a certain state - its half-life - and (2) the time it spends when passing through a subsystem - its transit-time. We discuss two approaches to quantify the half-life, present the novel method of in silico labeling, and introduce the label half-life and label transit-time. The developed method has been motivated by laboratory tracer experiments. To investigate the kinetic properties and behavior of a substance of interest, we computationally label this species in order to track it throughout its life cycle. The corresponding mathematical model is extended by an additional set of reactions for the labeled species, avoiding any double-counting within closed circuits, correcting for the influences of upstream fluxes, and taking into account combinatorial multiplicity for complexes or reactions with several reactants or products. A profile likelihood approach is used to estimate confidence intervals on the label half-life and transit-time.

Results

Application to the JAK-STAT signaling pathway in Epo-stimulated BaF3-EpoR cells enabled the calculation of the time-dependent label half-life and transit-time of STAT species. The results were robust against parameter uncertainties.

Conclusions

Our approach renders possible the estimation of species and label half-lives and transit-times. It is applicable to large non-linear systems and an implementation is provided within the PottersWheel modeling framework (http://www.potterswheel.de).

Thomas Maiwald, Julie Blumberg contributed equally to this work.

http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-6-13

4-https://books.google.ps/books?id=IEMe5wbfO78C&pg=PA83&lpg=PA83&dq=In-silico++residence+time&source=bl&ots=vRwcVw5Sgr&sig=DPxJdNS0kgGDRbVi0kX00VPHu5s&hl=en&sa=X&ved=0ahUKEwj1z9CKiKfLAhWIOhoKHbLKBV4Q6AEISjAI#v=onepage&q=In-silico%20%20residence%20time&f=false

Hoping this will be helpful,

Rafik

Article Correlation Between In Silico Physicochemical Characteristic...

Any such methodology will have to take into account the importance of protein conformational dynamics for achieving long residence time. See this paper for a discussion of this topic.

http://www.epizyme.com/wp-content/uploads/2014/06/Copeland_FutureMedChem_2011.pdf