1 Separation Science Group, Department of Organic and ...2 Laboratory for Organic and Bioorganic...
Transcript of 1 Separation Science Group, Department of Organic and ...2 Laboratory for Organic and Bioorganic...
New pharmaceutical compound
High oral bioavailability is desired
Good oral absorption is the first requirement
Interest in predicting human intestinal absorption (HIA) [1]
Effect on the central nervous system (CNS)?
Several mechanisms regulating drug permeability to the brain
Blood-brain barrier (BBB) is the most important
High BBB permeability indication for CNS effect
Common measure: log 𝐵𝐵 = log(𝐶𝑏𝑟𝑎𝑖𝑛
𝐶𝑏𝑙𝑜𝑜𝑑) [2]
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Indomethacin
Synthesis of a phosphocholine based lipid for improved
micellar LC based in vitro predictions of human intestinal
absorption and blood-brain barrier partitioning
M. De Vrieze1, P. Janssens2, R. Szucs3, J. Van der Eycken2, F. Lynen1
1 Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4-bis, B-9000 Ghent 2 Laboratory for Organic and Bioorganic Synthesis, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4, B-9000 Ghent 3 Pfizer Global R&D, Sandwich CT13 9NJ, Kent, United Kingdom
INTRODUCTION
The log BB model performed better compared to HIA
prediction, although data provided by MLC with miltefosine as
surfactant contributed in a positive way to both models.
This approach shows potential as an alternative or
complementary MLC strategy to predict in vivo behavior.
Additional research, using a variety of (phospho)lipids as
surfactant for MLC, might be very interesting, since this could
better mimic the composition of biological membranes.
REFERENCES
Figure 1: Drug interactions in MLC. Above the
CMC, retention depends on interactions with the
modified stationary phase and with the micelles
present in the mobile phase.
[1] Hou TJ, Wang JM, Zhang W, Xu XJ (2007) J Chem Inf Model 47 (1):208-218.
[2] Fu XC, Wang GP, Shan HL, Liang WQ, Gao JQ (2008) Eur J Pharm Biopharm 70 (2):462-466.
[3] Molero-Monfort M, Escuder-Gilabert L, Villanueva-Camañas RM, Sagrado S, Medina-Hernández MJ (2001) J Chrom B 753
:225-236
[4] Zhang QH, Horst R, Geralt M, Ma XQ, Hong WX, Finn MG, Stevens RC, Wuthrich K (2008) J Am Chem Soc 130 (23):7357-
7363.
RESULTS & DISCUSSION
CONCLUSION
EXPERIMENTAL
MLC
The retention factors (k) of the compounds were determined
Several molecular descriptors were added to the model Total molar charge Molecular weight Molar refractivity
Molar volume Parachor Polarizability
Log P Log D 7.4 Intrinsic aqueous solubility
pH solubility profile Plasma protein binding Ames test mutagenic index
Human intestinal absorption Polar surface area Hydrogen bond acceptor
Hydrogen bond donor Molecular surface area
Partial least squares (PLS) regression
Correlation coefficient (R) between actual (in vivo) and predicted values
Selecting the most relevant descriptors: monitor effect on the leave-one-
out cross-validation (LOOCV) regression coefficients upon systematic
removal and/or reinsertion of all descriptors from the models
Both models: remove Molar refractivity Molar volume
Log P Log D 7.4
Ames test mutagenic index Hydrogen bond donor
HIA and log BB
0.01 M miltefosine was dissolved in the mobile phase. The pH was adjusted
with a phosphate buffer at pH 7.4. The osmotic pressure was reproduced by
addition of NaCl (9.20 g/L). Column & flow rate: GraceSmart C18 column (3
µm, 150 mm x 2.1 mm) at 37 °C, flow rate 0.2 ml/min.
In vitro HIA and log BB prediction using
Micellar liquid chromatography (MLC)
RPLC; surfactant above critical micellar
concentration (CMC) in mobile phase
Secondary equilibrium (Figure 1) [3]
Stationary phase bulk solvent
Micelles bulk solvent
Retention time + descriptors model
Thus far in MLC: SDS, Brij35, CTAB were
used as surfactants.
Not comparable to membrane lipids
Miltefosine (Figure 2) is presented here
as an alternative MLC-surfactant
Model construction based on log k + computed descriptors
HIA prediction experimental HIA values
Log BB prediction experimental log BB values
Finally: model evaluation
Figure 2: Synthesis of miltefosine.
Synthesis of miltefosine
The synthesis route (Figure 2) was slightly modified from a previously
reported procedure by Zhang et al. [4]. HPLC-TOF-MS, 1H-NMR and 13C-
NMR were used for structure confirmation.
The results from the PLS and LOOCV regressions before and after
elimination of superfluous molecular descriptors are presented in Table 1. The
large difference in correlation coefficient before optimization is an indication of
overfitting in the model. By removing unnecessary descriptors, the overfitting
was reduced a lot. The final correlation coefficient of HIA (0.7175; based on
36 compounds) was lower than that for log BB (0.7849; based on 48
compounds). For both predictions, data provided by MLC with miltefosine
proved to contribute in a positive way.
The correlation between actual and predicted HIA and log BB values is
illustrated in Figure 4 before and after optimization. Although there are a few
outsiders, the predicted values for most compounds are close to the actual (in
vivo) determined values.
Figure 3: Chromatograms for some compounds using MLC with miltefosine as surfactant
To illustrate the retention behavior in purely aqueous MLC with 0.01 M
miltefosine, some chromatograms are presented in Figure 3.
HIA Log BB
R(PLS) 0.8237 0.8827
R(LOOCV) 0.3666 0.5298
HIA Log BB
R(PLS) 0.7991 0.8484
R(LOOCV) 0.7175 0.7849
Before optimization
After optimization
Table 1: Correlation coefficients between actual
and predicted HIA and log BB values using PLS
and LOOCV before and after optimization of
molecular descriptors.
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Actual log BB
R = 0.7849
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R = 0.7175
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Figure 4: Visual representation of the correlation between actual
and predicted log BB values using the LOOCV method before and
after elimination of superfluous molecular descriptors.
Prediction of HIA and log BB values
Each PLS regression leads to an equation, generally written as Y = b0 + b1 X1
+ b2 X2 + … + bn Xn, where Y can be HIA or log BB, and X1 … Xn are the
molecular descriptors. The coefficients (b0 … bn) for the two models are
divers, reflecting the difference between HIA and BBB permeation.