In vitro to in vivo extrapolation

In vitro to in vivo extrapolation refers to the qualitative or quantitative transposition of experimental results or observations made in vitro to predicts phenomena in vivo, on full living organisms.

The problem of transposing in vitro results is particularly acute in areas such as toxicology where animal experiments are being increasingly replaced by alternative tests.

Results obtained from in vitro experiments cannot usually be transposed as is to predict the reaction of an entire organism in vivo. Build a consistent and reliable extrapolation procedure from in vitro results to in vitro is therefore extremely important. Two solutions are now commonly accepted:

The two approaches are not incompatible: better in vitro systems will provide better data to mathematical models. On the other hand, increasingly sophisticated in vitro experiments collect increasingly numerous, complex, and challenging data to integrate: Mathematical models, such as systems biology models are much needed here.

Pharmacology

IVIVE in pharmacology can be used to assess pharmacokinetics (PK) or pharmacodynamics (PD)..

Since the timing and intensity of effects on a given target depend on the concentration time course of candidate drug (parent molecule or metabolites) at that target site, in vivo tissue and organ sensitivities can be completely different or even inverse of those observed on cells cultured and exposed in vitro. That indicates that extrapolating effects observed in vitro needs a quantitative model of in vivo PK. It is generally accepted that physiologically based PK (PBPK) models are central to the extrapolations.[3]

In the case of early effects or those without inter-cellular communications, it is assumed that the same cellular exposure concentration cause the same effects, both qualitatively and quantitatively, in vitro and in vivo. In these conditions, it is enough to (1) develop a simple pharmacodynamics model of the dose–response relationship observed in vitro and (2) transpose it without changes to predict in vivo effects.[4]

However, cells in cultures do not mimic perfectly cells in a complete organism. To solve that extrapolation problem, more statistical models with mechanistic information are needed, or we can rely on mechanistic systems of biology models of the cell response. Those models are characterized by a hierarchical structure, such as molecular pathways, organ function, whole-cell response, cell-to- cell communications, tissue response and inter-tissue communications.[5]

References

  1. ↑ Sung, JH; Esch, MB; Shuler, ML (2010). "Integration of in silico and in vitro platforms for pharmacokinetic-pharmacodynamic modeling". Expert Opinions in Drug Metabolism and Toxicology. 6: 1063–1081. doi:10.1517/17425255.2010.496251.
  2. ↑ Quignot, Nadia; Bois, Frédéric Yves (2013). "A computational model to predict rat ovarian steroid secretion from in vitro experiments with endocrine disruptors". PLoS ONE. 8 (1): e53891. doi:10.1371/journal.pone.0053891.
  3. ↑ Yoon M, Campbell JL, Andersen ME, Clewell HJ (2012). "Quantitative in vitro to in vivo extrapolation of cell-based toxicity assay results". Critical Reviews in Toxicology.
  4. ↑ Louisse J, de Jong E, van de Sandt JJ, Blaauboer BJ, Woutersen RA, Piersma AH, Rietjens IM, Verwei M (2010). "The use of in vitro toxicity data and physiologically based kinetic modeling to predict dose–response curves for in vivo developmental toxicity of glycol ethers in rat and man". Toxicological Sciences. 118: 470–484. doi:10.1093/toxsci/kfq270.
  5. ↑ Hunt CA, Ropella GE, Lam TN, Tang J, Kim SH, Engelberg JA, Sheikh-Bahaei S (2009). "At the biological modeling and simulation frontier". Pharmaceutical Research. 26: 2369–2400. doi:10.1007/s11095-009-9958-3.
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