To Give Chinese Children "a Memorable China":the Trend of Chinese Indigenous Picture Books

To investigate if drug solubility in pharmaceutical excipients used in lipid based formulations (LBFs) can be predicted from physicochemical properties. Solubility was measured for 30 structurally diverse drug molecules in soybean oil (SBO, long-chain triglyceride; TG(LC)), Captex355 (medium-chain triglyceride; TG(MC)), polysorbate 80 (PS80; surfactant) and PEG400 co-solvent and used as responses during PLS model development. Melting point and calculated molecular descriptors were used as variables and the PLS models were validated with test sets and permutation tests. Solvation capacity of SBO and Captex355 was equal on a mol per mol scale (R (2) = 0.98). A strong correlation was also found between PS80 and PEG400 (R (2) = 0.85), identifying the significant contribution of the ethoxylation for the solvation capacity of PS80. In silico models based on calculated descriptors were successfully developed for drug solubility in SBO (R (2) = 0.81, Q (2) = 0.76) and Captex355 (R (2) = 0.84, Q (2) = 0.80). However, solubility in PS80 and PEG400 were not possible to quantitatively predict from molecular structure. Solubility measured in one excipient can be used to predict solubility in another, herein exemplified with TG(MC) versus TG(LC), and PS80 versus PEG400. We also show, for the first time, that solubility in TG(MC) and TG(LC) can be predicted from rapidly calculated molecular descriptors

Computational and Experimental Models for the Prediction of Intestinal Drug Solubility and Absorption

New effective experimental techniques in medicinal chemistry and pharmacology have resulted in a vast increase in the number of pharmacologically interesting compounds. However, the number of new drugs undergoing clinical trial has not augmented at the same pace, which in part has been attributed to poor absorption of the compounds. The main objective of this thesis was to investigate whether computer-based models devised from calculated molecular descriptors can be used to predict aqueous drug solubility, an important property influencing the absorption process. For this purpose, both experimental and computational studies were performed. A new small-scale shake flask method for experimental solubility determination of crystalline compounds was devised. This method was used to experimentally determine solubility values used for the computational model development and to investigate the pH-dependent solubility of drugs. In the computer-based studies, rapidly calculated molecular descriptors were used to predict aqueous solubility and the melting point, a solid state characteristic of importance for the solubility. To predict the absorption process, drug permeability across the intestinal epithelium was also modeled. The results show that high quality solubility data of crystalline compounds can be obtained by the small-scale shake flask method in a microtiter plate format. The experimentally determined pH-dependent solubility profiles deviated largely from the profiles predicted by a traditionally used relationship, highlighting the risk of data extrapolation. The in silico solubility models identified the non-polar surface area and partitioned total surface areas as potential new molecular descriptors for solubility. General solubility models of high accuracy were obtained when combining the surface area descriptors with descriptors for electron distribution, connectivity, flexibility and polarity. The used descriptors proved to be related to the solvation of the molecule rather than to solid state properties. The surface area descriptors were also valid for permeability predictions, and the use of the solubility and permeability models in concert resulted in an excellent theoretical absorption classification. To summarize, the experimental and computational models devised in this thesis are improved absorption screening tools applicable to the lead optimization in the drug discovery process

Contemporary formulation development for inhaled pharmaceuticals

Pulmonary delivery has gained increased interests over the past few decades. For conditions within the respiratory tract, targeted drug delivery directly to the site of action can achieve a high local concentration for efficacy with reduced unwanted systemic exposure and adverse effect. For systemic conditions, the unique physiology of the lung evolutionarily designed for rapid gaseous exchange presents an entry route for systemic drug delivery. Although the development of inhaled formulations has come a long way over the last few decades, many aspects of it remain to be elucidated. In particular, a reliable and well-understood method for in vitro-in vivo correlations remains to be established. With the rapid and ongoing advancement of technology, there is much potential to better utilise computational methods including different types of modelling and simulation approaches to support inhaled formulation development. This review intends to provide an introduction on some fundamental concepts in pulmonary drug delivery and inhaled formulation development followed by discussions on some challenges and opportunities in the translation of inhaled pharmaceuticals from preclinical studies to clinical development. The review concludes with some recent advancements in modelling and simulation approaches that could play an increasingly important role in contemporary formulation development of inhaled pharmaceuticals

Suitability of Artificial Membranes in Lipolysis-Permeation Assays of Oral Lipid-Based Formulations

Purpose To evaluate the performance of artificial membranes in in vitro lipolysis-permeation assays useful for absorption studies of drugs loaded in lipid-based formulations (LBFs). Methods Polycarbonate as well as PVDF filters were treated with hexadecane, or lecithin in n-dodecane solution (LiDo) to form artificial membranes. They were thereafter used as absorption membranes separating two compartments mimicking the luminal and serosal side of the intestine in vitro. Membranes were subjected to dispersions of an LBF that had been digested by porcine pancreatin and spiked with the membrane integrity marker Lucifer Yellow (LY). Three fenofibrate-loaded LBFs were used to explore the in vivo relevance of the assay. Results Of the explored artificial membranes, only LiDo applied to PVDF was compatible with lipolysis by porcine pancreatin. Formulation ranking based on mass transfer in the LiDo model exposed was the same as drug release in single-compartment lipolysis. Ranking based on observed apparent permeability coefficients of fenofibrate with different LBFs were the same as those obtained in a cell-based model. Conclusions The LiDo membrane was able to withstand lipolysis for a sufficient assay period. However, the assay with porcine pancreatin as digestive agent did not predict the in vivo ranking of the assayed formulations better than existing methods. Comparison with a Caco-2 based assay method nonetheless indicates that the in vitro in vivo relationship of this cell-free model could be improved with alternative digestive agents

Early drug development predictions of glass-forming ability and physical stability of drugs

The purpose of this study was to investigate if rapidly measured physical properties can predict glass-forming ability and glass stability of drug compounds. A series of 50 structurally diverse drug molecules were studied with respect to glass-forming ability and, for glass-formers (n = 24), the physical stability upon 1 month of storage was determined. Spray-drying and melt-cooling were used to produce the amorphous material and the solid state was analysed by Differential Scanning Calorimetry (DSC) and Powder X-ray Diffraction. Thermal properties and molecular weight (Mw) were used to develop predictive models of (i) glass-forming ability and (ii) physical stability. In total, the glass-forming ability was correctly predicted for 90% of the drugs from their Mw alone. As a rule of thumb, drugs with Mw greater than 300 g/mole are expected to be transformed to its amorphous state by using standard process technology. Glass transition temperature and Mw predicted the physical stability upon storage correctly for 78% of the glass-forming compounds. A strong sigmoidal relationship (R-2 of 0.96) was identified between crystallization temperature and stability. These findings have the potential to rationalize decisions schemes for utilizing and developing amorphous formulations, through early predictions of glass-forming ability from Mw and physical stability from simple DSC characterization

Computational modeling to predict the functions and impact of drug transporters

Transport proteins are important mediators of cellular drug influx and efflux and play crucial roles in drug distribution, disposition and clearance. Drug-drug interactions have increasingly been found to occur at the transporter level and, hence, computational tools for studying drug-transporter interactions have gained in interest. In this short review, we present the most important transport proteins for drug influx and efflux. Computational tools for predicting and understanding the substrate and inhibitor interactions with these membrane-bound proteins are discussed. We have primarily focused on ligand-based and structure-based modeling, for which the state-of-the-art and future challenges are also discussed

Computational prediction of drug solubility in water-based systems : qualitative and quantitative approaches used in the current drug discovery and development setting

In this review we will discuss recent advances in computational prediction of solubility in water-based solvents. Our focus is set on recent advances in predictions of biorelevant solubility in media mimicking the human intestinal fluids and on new methods to predict the thermodynamic cycle rather than prediction of solubility in pure water through quantitative structure property relationships (QSPR). While the literature is rich in QSPR models for both solubility and melting point, a physicochemical property strongly linked to the solubility, recent advances in the modelling of these properties make use of theory and computational simulations to better predict these properties or processes involved therein (e.g. solid state crystal lattice packing, dissociation of molecules from the lattice and solvation). This review serves to provide an update on these new approaches and how they can be used to more accurately predict solubility, and also importantly, inform us on molecular interactions and processes occurring during drug dissolution and solubilisation

Models for Predicting Drug Absorption From Oral Lipid-Based Formulations

In this review, we describe the in vitro tools currently used to identify when a lipid-based formulation has the potential to deliver a poorly water-soluble drug via the oral route. We describe the extent to which these tools reflect the in vivo performance of the formulation and, more importantly, we present strategies that we foresee will improve the in vitro-in vivo correlations. We also present emerging computational methods that are likely to allow large parts of the formulation development to be carried out in the computer rather than in the test tube. We suggest that these computational tools will also improve the mechanistic understanding of in vivo formulation performance in the complex and dynamic environment of the gut

Molecular Drivers of Crystallization Kinetics for Drugs in Supersaturated Aqueous Solutions

In this study, we explore molecular properties of importance in solution-mediated crystallization occurring in supersaturated aqueous drug solutions. Furthermore, we contrast the identified molecular properties with those of importance for crystallization occurring in the solid state. A literature data set of 54 structurally diverse compounds, for which crystallization kinetics from supersaturated aqueous solutions and in melt-quenched solids were reported, was used to identify molecular drivers for crystallization kinetics observed in solution and contrast these to those observed for solids. The compounds were divided into fast, moderate, and slow crystallizers, and in silico classification was developed using a molecular K-nearest neighbor model. The topological equivalent of Grav3 (related to molecular size and shape) was identified as the most important molecular descriptor for solution crystallization kinetics; the larger this descriptor, the slower the crystallization. Two electrotopological descriptors (the atom-type E-state index for -Caa groups and the sum of absolute values of pi Fukui(+) indices on C) were found to separate the moderate and slow crystallizers in the solution. The larger these descriptors, the slower the crystallization. With these 3 descriptors, the computational model correctly sorted the crystallization tendencies from solutions with an overall classification accuracy of 77% (test set)