De Vrieze, Mike

Janssens, Pieter

Lynen, Frederic

Szucs, Roman

Van der Eycken, Johan

English

Ghent University Academic Bibliography

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⁡(𝐶𝑏𝑟𝑎𝑖𝑛𝐶𝑏𝑙𝑜𝑜𝑑) [2] Time (min) -2000030000800001300001800002300002800003300000 2 4 6 8 10 12 14 16 18 2035  30  25  20  15  10  5  0 Intensity (mV) Acetylsalicylic acid -10000010000200003000040000500000 10 20 30 40 50 606  5  4  3  2  1  0 Time (min) Intensity (mV) Halothane -50000-30000-100001000030000500000 10 20 30 40 50 60 70 80 9010  8  6  4  2  0 Time (min) Intensity (mV) Ibuprofen -10000001000002000003000004000005000006000000 10 20 30 40 50 607   6   5   4   3   2   10  0 Time (min) Intensity (mV) Amobarbital Time (min) -400001600003600005600007600009600000 2 4 6 8 10 12 14 16 18 201 0  80  60  40  20  0 Intensity (mV) Acetaminophen -500000500001000001500002000002500003000000 10 20 30 40 50 60 70 80 9035  30  25  2   15  1   5   Time (min) Intensity (mV) 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. 40 50 60 70 80 90 10040506070809010011040 50 60 70 80 90 10030405060708090100110120130-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-2-1.5-1-0.500.511.5-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-2-1.5-1-0.500.511.522.53Actual HIAR = 0.366632.521.510.50-0.5-1-1.5-2Predictedlog BBActual log BBR = 0.5298-2          -1.5          -1          -0.5           0           0.5            1           1.5           2Actual log BBR = 0.7849-2          -1.5          -1          -0.5           0           0.5            1           1.5           2Actual HIAR = 0.717540                50                60                70                80                90               100HIA Log BBBeforeoptimizationAfteroptimization1.510.50-0.5-1-1.5-2Predictedlog BB110100908070605040PredictedHIA13012011010090807060504030PredictedHIA40              50                60                70                80               90               100Figure 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. 

Synthesis of a phosphocholine based lipid for improved micellar LC based in vitro predictions of human intestinal absorption and blood-brain barrier partitioning

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Synthesis of a phosphocholine based lipid for improved micellar LC based in vitro predictions of human intestinal absorption and blood-brain barrier partitioning

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