Lipophilicity prediction

The E-state indices [72, 73] were developed to cover both topological and valence states of atoms. These indices were successfully used to build correlations between the structure and activity for different physicochemical and biological properties [72]. New applications of this methodology are also extensively reviewed in Ghapter 4. Several articles by different authors demonstrated the applicability of these indices for lipophilicity predictions [74—83]. 

When a validated hit is selected as a promising lead compound, its physicochemical profile must be studied in detail. Sophisticated in silica approaches such as 3D lipophilicity predictions coupled with extensive conformational analysis [49, 50,135,146] and molecular field interactions (MIFs) [147-150] could be helpful to better interpret the detailed experimental investigations of their ionization constants by capillary electrophoresis or potentiometric titrations [151, 152] and their lipophilicity profiles by potentiometry [153]. However, these complex approaches cannot be performed easily on large number of compounds and are generally applied only on the most promising compounds. 

K, V N Viswanadhan and J J Wendoloski 1998. Prediction of Hydrophobic (Lipophilic) lerties of Small Organic Molecules Using Fragmental Methods An Analysis of ALOGP and GP Methods. Journal of Physical Chemistry 102 3762-3772. 

The bioaccessibility of a compound can be defined as the result of complex processes occurring in the lumen of the gut to transfer the compound from a non-digested form into a potentially absorbable form. For carotenoids, these different processes include the disruption of the food matrix, the disruption of molecular linkage, the uptake in lipid droplets, and finally the formation and uptake in micelles. Thus, the bioaccessibility of carotenoids and other lipophilic pigments from foods can be characterized by the efficiency of their incorporation into the micellar fraction in the gut. The fate of a compound from its presence in food to its absorbable form is affected by many factors that must be known in order to understand and predict the efficiency of a compound s bioaccessibility and bioavailability from a certain meal. ... 

The distribution of a drug in the body is largely driven by its physicochemical properties and in part for some compounds by the contribution of transporter proteins [17]. By using the Oie-Tozer equation and estimates for ionization (pfCj). plasma protein binding (PPB) and lipophilicity (log quite robust predictions for the volume of distribution at steady state (Vdss), often within 2-fold of the observed value, can be made [18]. 

From these equations it is possible to predict the effective lipophilicity (log D) of an acidic or basic compound at any pH value. The data required in order to use the relationship in this way are the intrinsic lipophilicity (log P), the dissociation constant (pKa) and the pH of the aqueous phase. The overaU effect of these relahonships is the effechve hpophilicity of a compound, at physiological pH, is approximately the log P value minus one unit of hpophilicity, for every unit of pH the pKa value is below (for acids) and above (for bases) pH 7.4. Obviously for compounds with mul-hfunchonal ionizable groups the relahonship between log P and log D, as weU as log D as a function of pH become more complex [65, 68, 70]. For diprotic molecules there are already 12 different possible shapes of log D-pH plots. 

The attraction of lipophilicity in medicinal chemistry is mainly due to Corwin Hansch s work and thus it is traditionally related to pharmacodynamic processes. However, following the evolution of the drug discovery process, lipophilicity is today one of the most relevant properties also in absorption, distribuhon, metabolism, excretion and toxicity (ADMET) prediction, and thus in drug profiling (details are given in Chapter 2). 

Lipophilicity is intuitively felt to be a key parameter in predicting and interpreting permeability and thus the number of types of lipophilicity systems under study has grown enormously over the years to increase the chances of finding good mimics of biomembrane models. However, the relationship between lipophilicity descriptors and the membrane permeation process is not clear. Membrane permeation is due to two main components the partition rate constant between the lipid leaflet and the aqueous environment and the flip-flop rate constant between the two lipid leaflets in the bilayer [13]. Since the flip-flop is supposed to be rate limiting in the permeation process, permeation is determined by the partition coefficient between the lipid and the aqueous phase (which can easily be determined by log D) and the flip-flop rate constant, which may or may not depend on lipophilicity and if it does so depend, on which lipophilicity scale should it be based ... 

To sum up, lipophilicity is only one component of permeability, and thus any relationships found between passive permeation and log D] are reliable for the investigated series of compounds, but cannot be used to make general predictions. 

The Jamieson paper reports the results of a number of studies, some successful, others not. Failures can be ascribed to the difficulties encountered in log P control. The first evident trouble concerns the choice of the lipophilicity descriptor many prefer log P, but this choice is questionable as has been outlined by Lombardo (see Chapter 16). Secondly, variations in lipophilicity profile influence not only hERG activity, but also target selectivity and also ADMET properties. Lipophilicity is a bulk property and its modification can involve different moieties of the molecules. Once the chemical modulation has been designed, but before moving to the bench, the research group should predict the consequences of this change on each step of the drug s action, but unfortunately this is not always done. 

P., Ghlenov, M., McConnell, O., Ghait, A., Kipnis, V., Zaslavsky, B. Relative hydrophobicity and lipophilicity of drugs measured by aqueous two-phase partitioning, octanol-buffer partitioning and HPLG. A simple model for predicting blood-brain distribution. Eur. J. Med. 

Hansch and Leo [13] described the impact of Hpophihdty on pharmacodynamic events in detailed chapters on QSAR studies of proteins and enzymes, of antitumor drugs, of central nervous system agents as well as microbial and pesticide QSAR studies. Furthermore, many reviews document the prime importance of log P as descriptors of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties [5-18]. Increased lipophilicity was shown to correlate with poorer aqueous solubility, increased plasma protein binding, increased storage in tissues, and more rapid metabolism and elimination. Lipophilicity is also a highly important descriptor of blood-brain barrier (BBB) permeability [19, 20]. Last, but not least, lipophilicity plays a dominant role in toxicity prediction [21]. 

Ghose, A. K., Viswanadhan, V. N., Wendoloski,).). Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods an analysis of... 

The application of Eq. (6) to predict lipophilicity for compounds with several functional groups runs into problems. The difficulties are associated with intramolecular interactions, which could not be addressed by addihve schemes as used in the SLIPPER model. Therefore, the authors correct the logP prediction of a given molecule according to the lipophilicity values of the nearest neighbors by using cosine similarity measures and molecular fragments [13, 14]. 

Tetko, I. V., Tandiuk, V. Y. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program. 

However, there is still a strong need to develop new methods that will be able to quantitatively or at least qualitatively estimate the prediction accuracy of log D models. Such models will allow the computational chemist to distinguish reliable versus nonreliable predictions and to decide whether the available model is sufficiently accurate or whether experimental measurements should be provided. For example, when applying ALOGPS in the LIB RARY model it was possible to predict more than 50% and 30% compounds with an accuracy of MAE <0.35 for Pfizer and AstraZeneca collections, respectively [117]. This precision approximately corresponds to the experimental accuracy, s=0.4, of potentiometric lipophilicity determinations [15], Thus, depending on the required precision, one could skip experimental measurements for some of the accurately predicted compounds. 

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