(W1030-10-57) In Silico Tuning of API for Liposomal Drug Delivery

Purpose: Liposomes are intensively studied as carriers of various types of active pharmaceutical ingredients (APIs). Unfortunately, some APIs are not appropriate for formulation in the liposome because of their physicochemical properties, particularly permeability and partitioning. This problem has three solutions: (I) a change of liposome, (II) the addition of permeability enhancer, and (III) a small modification of the structure of API. We decided to go the third way. The MolMeDB [1] is a comprehensive and interactive database about interactions of (small) molecules with membranes, including permeability data. Because of the non-existence of a large amount of data about the behavior of APIs in the liposomes, we analyzed data in the MolMeDB database. Our current paper [2] created the Liposome Biochemical Classification System (LBCS). LBCS defines areas of usability of the APIs for the liposomal formulation according to their permeability and partitioning. Based on the data analysis from the MolMeDB database, we tried to determine the influence of functional groups on the permeability and partitioning of small molecules and the possibility of controlling the usability of the molecules for liposomal formulation.

Methods: We analyzed permeability data from MolMeDB; see our companion poster of the new developments of the MolMeDB database). We included permeability data from the three methods - two experimental (CACO-2 and PAMPA) and one calculated (COSMOperm). The particular steps of analysis are available in Rscript. Cytarabine analogs were prepared by MarvinSketch 21.9 [3]. Permeabilities of uncharged cytarabine and its analogs were calculated by COSMOperm X8 [4]. Partition coefficients were calculated using COSMOmic X8 software. DPPC:DPPG:Cholesterol – 75:10:15 mol% membrane at 293 K was used as the model membrane.

Results: The first step was analyzing the permeability data available in the MolMeDB.  If we plot the permeations against each other (Graphic 1), the correlation between individual methods is unfortunately bad, even between both experimental methods. The reason why we see these huge inconsistencies is in the character and limitations of these methods. As shown in histogram A (Graphic 2), only the COSMOperm can distinguish rapidly permeating molecules. The CACO-2 values are in a very narrow range of values from -8 to -4 log units due to the physical limits of the measurement. In histogram B (Graphic 2), we can observe PAMPA values even for faster permeating molecules (less than -4 log units). This phenomenon is caused by the existence of multiple different permeabilities reported as the PAMPA values - intrinsic, apparent, etc., which are not always reported clearly in the literature. Based on the analysis of methods, we conclude that only the COSMOperm method is suitable for calculating the permeation of molecules on all scales, and hence this method is the best choice for determining the influence of functional groups on the permeability. Secondly, we chose a very poorly permeating molecule of cytarabine as a candidate molecule for studying the influence of functional groups on drug permeability. We considered 3 different cytarabine modifications and prepared ester, amide, and alkyl analogs of cytarabine. Graphic 3 shows the calculated permeabilities of cytarabine and its analogs in the context of LBCS. We can clearly recognize that the analogs are more permeable and more lipophilic. Also, the analogs show a linear dependency of permeation coefficient to the number of attached carbons, while the dependence of partitioning is not linear.

Conclusion: We analyzed over 737 records of permeability data from three common methods. Based on the analysis, we concluded that only COSMOperm is suitable for studying the permeability of poorly permeated molecules. In the example of cytarabine, we show the influence of three chemical modifications and a change in permeability which is necessary for the liposomal formulation of cytarabine.

References: [1] Juračka, J.; Šrejber, M.; Melíková, M.; Bazgier, V.; Berka, K., MolMeDB: Molecules on Membranes Database. Database 2019, 2019.
[2] Balouch, M.; Šrejber, M.; Šoltys, M.; Janská, P.; Štěpánek, F.; Berka, K., In silico screening of drug candidates for thermoresponsive liposome formulations. Molecular Systems Design & Engineering 2021, 6 (5), 368-380.
[3] Schwöbel, J. A. H.; Ebert, A.; Bittermann, K.; Huniar, U.; Goss, K.-U.; Klamt, A., COSMOperm: Mechanistic Prediction of Passive Membrane Permeability for Neutral Compounds and Ions and Its pH Dependence. The Journal of Physical Chemistry B 2020, 124 (16), 3343-3354

Acknowledgments: Authors acknowledge support from Palacky University Olomouc (project IGA_PrF_2022_019 and subproject DSGC-2021-0060 within OP VVV CZ.02.2.69/0.0/0.0/19_073/0016713) and ELIXIR-CZ infrastructure (project LM2018131)

Permeation rates of molecules that are present in A: CACO-2 and COSMOperm dataset; B: PAMPA and COSMOperm dataset, C: PAMPA and CACO-2 dataset. Each point represents one molecule that has permeation data from both methods. The full line represents the equivalence of permeation data, dashed lines represent the 1 logPerm difference between the methods



Distribution of considered permeation data (neutral molecules, 25 °C, smaller than 800 Da) from MolMeDB database for A - COSMOperm method, B - PAMPA method, C - CACO-2 method. All data is displayed in log of cm/s.



A - Cytarabine and its prodrugs in LBCS parametric space as defined in [2]. The squares, circles and triangles represent three lines of cytarabine analogues differing in the length of a carbon chain. The number in the object represents the number of carbons added. In the case of squares and circles, the additions are in the form of acyls, in the case of triangles, the additions are alkyl chains. B - Structure of cytarabine with highlighted positions of alkylation (light purple), esterification (light blue) and amidation (dark yellow)