The document discusses the extrapolation of pharmacokinetic data from preclinical to clinical settings, detailing methods such as allometric scaling and physiology-based pharmacokinetic modeling to predict human drug dosage, clearance, volume of distribution, and bioavailability. It outlines various methodologies for calculating these parameters, addressing the limitations of empirical approaches and interspecies differences. Finally, it highlights the challenges in predicting human pharmacokinetics from animal data due to anatomical and physiological variations.

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2. EXTRAPOLATION OF
PRECLINICAL DATA TO
CLINICAL DATA
Presented by
Vincy Dinakaran
M.Pharm 1st sem
Roll number :11
DPS, Cheruvandoor.
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3. EXTRAPOLATION
It is the prediction of effects in humans from effects in animals using
pharmacokinetics and pharmacodynamics studies.
Extrapolation of drug dose or pharmacokinetic parameter to a species of interest is
called scaling.
Scaling is of two types:
ISOMETRIC SCALING – dose for one species is applied to all
ALLOMETRIC SCALING – correlating mass of an organism with physiological
parameters
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5. ALLOMETRIC SCALING
 It was a technique developed to explain the observed relationship between organ
size and body weight of mammals.
Allometric scaling of pharmacokinetic data typically focus on interspecies
relationship between clearance and volume of distribution of unbound drug and
species body weight. These are then extrapolated to humans.
DISADVANTAGES
1. Purely empirical.
2. Cannot give any idea about the mechanism of action.
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6. PBPK (Physiology based pharmacokinetic)
modelling
It make use of data such as prediction of clearance and tissue distribution from
in- vitro and in-silico data.
It is useful in predicting plasma and tissue concentration time profiles.
Also useful in addressing issues in pharmacokinetics (eg: saturable metabolism)
and inter-species differences in nature, expression levels and dissipation of
pharmacological effects.
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7. 1. Methods for predicting human volume of distribution
2. Methods for predicting human clearance
3. Methods for predicting human t ½
4. Methods for predicting human oral bioavailability
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8. ● Average fraction unbound in tissue method
● Proportionality
● Allometry without protein binding
● Allometry corrected for protein binding
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9. • Uses experimentally determined value for volume of distribution and plasma protein
binding for each species , along with standard values for extracellular fluid volumes ,
plasma volumes and calculate the fraction unbound in tissues in animal species
• Fut = Vr Fu
[VDss – Vp (Fu Ve)] – [(1-Fu)(Re/i)Vp]
• Avg Fut of different species is calculated and averaged.
• Average will be equal to human Fut
• Experimentally determine human Fu
• VD(human prediction)= Vp+ [Fu(human) x Ve] + {[1-Fu(human) ]x (Re/i)x Vp} +Vr {Fu(human) / Fut(avg) }
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10. Proportionality could be setup between the fraction of drug in plasma in dog and the
human and the volume of distribution in these 2 species.
The assumption was that the tissue binding of drug is similar in dogs and humans
and that physiological parameters such as extracellular fluid volumes are similar
between the 2 species.
VD (human prediction) = Fu(human). VD(dog)
Fu(dog)
Fu – fraction of unbound drug in the plasma of dog and human
VD(dog) –volume of distribution at steady state in dog
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11. The physiological parameters used in the scaling was total body weight.
In this method the plots were constructed of total volume of distribution in preclinical
species vs animal body weight on a log-log scale for each compound in the analysis.
This equation were obtained by linear regression of the data points to determine the
values a and b for each compound.
 These were used along with a standard value for human body weight (70 kg) to predict
human volume of distribution.
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log10 VD=a. log10 body weight (kg)+b

12. An identical approach was taken as described above except that animal volume of distribution were
corrected for plasma protein binding.
 This method was used to yield free volume of distribution.
 The values were plotted as in previous method to determine the allometric relationship for free
volume of distribution vs total body weight.
 The projected human volume of distribution was then converted to total VD by following equation:
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VD (free) = VD (total) /fu
VD (total) = VD(free) .fu(human)

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2. METHODS FOR PREDICTING
HUMAN CLEARENCE
Approaches for predicting human
clearance
Methods in which first
order consumption of
parent drug was
monitored in liver
microsomal incubations
to yield in vitro t ½
values
Allometric scaling
methods with and
without consideration
of interspecies
differences in plasma
protein binding
Methods in which Vmax
and Km were
determined and used in
the calculation of Clint

14.  Intrinsic clearance can be calculated from in-vitro t1/2 data obtained from liver microsomes
(metabolic assay).
We can calculate the intrinsic clearance data with the help of integrated Michaelis-Menten equation
Vm .dt = Km+[S] .d[S]
[S]
When [S]=0.5[S]t=0 the following equation applies
Vm.t ½ = 0.693+0.5[s]t=0
Km Km
The substrate concentration used is well below the Km an assumption
0.5[S]
Km << <0.693
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Thus the equation degenerates to
Vm.t ½ = 0.693
Km
Vm = 0.693 = Clint
Km t ½
The invitro t ½ is incorporated into the following equation
Clint = 0.693 * liver weight
Invitro t ½ .liver in incubation. Fu(inc)
Invitro t ½ =min
Liver weight =g/kg of body weight
Liver in incubation=g of liver/ml
Therefore units of Clint=ml/min/kg

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The liver incubation value was calculated from the amount of protein in the incubation and a scale
up factor from protein to gram of body weight.
This equation indicates that the value for binding to protein in the incubation be included,
however in this treatment it was assumed to be zero ie, Fu(inc) =1.
Thus the intrinsic clearance value calculated were based on total concentrations
Clint = 0.693 * g of liver weight * ml incubation* 45mg of microsomal protein
t ½ * kg of body wt. * mg of microsomal protein * g of liver wt.

17.  Through this method we can find parameters like Km and Vmax.
These are measured from liver microsomal incubations.
 Clint = Vmax
Km
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18. i. With protein binding and MLP correction factor
 In allometric scaling of clearance the physiological parameter used is total body weight.
In case of this method correction for interspecies difference in both plasma protein binding and
MLP were applied.
For plasma protein binding free clearance is defined as
CLp(free) = CLp(total)
fu
Corrections for interspecies for both plasma protein binding and MLP were made.
Graph of log 10 CLp(free) v/s log 10 CLp(total) was plotted.
Then total clearance values for humans were calculated.
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ii. Without protein binding and without MLP correction factor
This was carried out as described above using total CL and body weight, with no
correction for interspecies difference in MLP
iii. With protein binding and without MLP protein correction factor
Allometric scaling was carried out using free Cl values and body weight, with no
corrections for interspecies difference in MLP.
Iv. Without protein binding and with MLP correction factor
Allometric scaling was done as described except that Cl values were not converted
to free Cl values.

20. 2 Methods :
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1. ANIMAL
CORRELATIONS
2. COMBINATIONS OF
HUMAN VOLUME AND
CLEARANCE
PREDICTIONS

21.  To construct correlation, measured t ½ value in animal were plotted vs human t ½ values and
functions were derived from 1/x-weighed linear regression.
 The prediction of human t ½ were then obtained by inserting the animal t ½ value into the
regression equation.
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22. Each method for predicting the volume of distribution was combined with each method of
predicting CL to generate predictions of human t ½ using the following equation:
Predicted human t ½ = 0.693* predicted human VD
Predicted human CLp
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23.  The method for predicting human oral bioavailability used those described for
clearance with the rearrangement equation that account only for first pass hepatic
clearance and accounted for neither the potential limitations on absorption from
the GI tract(ie, fraction absorbed Fa was assumed to be a unity)nor potential first
pass excretion by the gut wall tissue(Fg=1)
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F=Fa*Fg [1-Clp/Q]

24. Pharmacokinetic differences between the test and humans.
Idiosyncratic adverse events in humans, the mechanism of which are not understood and are not
demonstrate in animals by ordinary toxicological and pharmacological investigation.
Underlying pathological conditions- drugs may exacerbate underlying disease in humans that do
not exist in animals, relationships that exist between the drug and its metabolites on the one hand
and underlying disease on the other , cannot adequately be investigated or predicted from studies
conducted on healthy animals.
Species difference in anatomy and physiological function and in tolerance and enzyme
induction.
Adverse drug reaction that can only be communicated verbally and are not normally recognized
in animals.
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25. REFERENCE
R.Swtt obach,James G baxter,Theodore E Liston,B Michael Silber and Philip
Wastatt1997 The prediction of human pharmacokinetic parameters from
preclinical and invitro metabolism data,The journal of pharmacology aand
experimental therapeutics page number:46-51
Dong-Seok Yim ,Soo Hyeon Bae et.al. June 18 2021 Predicting human
pharmacokinetics from preclinical data: clearance Transl Clin Pharmacol. 2021
Jun;29(2):78-87 https://doi.org/10.12793/tcp.2021.29.e12
Go-Wun Choi ,Yong-Bok Lee et.al.5 April 2019 Interpretation of Non-Clinical
Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo
Extrapolation and Allometric Scaling
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