Critical Assessment of pH-Dependent Lipophilicity Profiles of Small Molecules: Which One Should We Use and In Which Cases?

The front cover artwork is provided by CBio3 Laboratory and Computational Toxicology and Artificial Intelligence Laboratory (LaToxCIA) both at the University of Costa Rica. The image shows the formalisms commonly used to determine the pH-dependent lipophilicity profile of ionizable compounds. Herein, for 4-phenylbutylamine it is accurately predicted when the apparent ion pair partitioning is considered. Read the full text of the Research Article at 10.1002/cphc.202300548.

Critical Assessment of pH-Dependent Lipophilicity Profiles of Small Molecules: Which One Should We Use and In Which Cases?

Lipophilicity is a physicochemical property with wide relevance in drug design, computational biology, food, environmental and medicinal chemistry. Lipophilicity is commonly expressed as the partition coefficient for neutral molecules, whereas for molecules with ionizable groups, the distribution coefficient (D) at a given pH is used. The logDpH is usually predicted using a pH correction over the logPN using the pKa of ionizable molecules, while often ignoring the apparent ion pair partitioning ( P IP app ) ${{\rm{(}}P_{{\rm{IP}}}^{{\rm{app}}} )}$ . In this work, we studied the impact of ( P IP app ) ${{\rm{(}}P_{{\rm{IP}}}^{{\rm{app}}} )}$ on the prediction of both the experimental lipophilicity of small molecules and experimental lipophilicity-based applications and metrics such as lipophilic efficiency (LipE), distribution of spiked drugs in milk products, and pH-dependent partition of water contaminants in synthetic passive samples such as silicones. Our findings show that better predictions are obtained by considering the apparent ion pair partitioning. In this context, we developed machine learning algorithms to determine the cases that P I app ${P_{\rm{I}}^{{\rm{app}}} }$ should be considered. The results indicate that small, rigid, and unsaturated molecules with logPN close to zero, which present a significant proportion of ionic species in the aqueous phase, were better modeled using the apparent ion pair partitioning ( P IP app ) ${{\rm{(}}P_{{\rm{IP}}}^{{\rm{app}}} )}$ . Finally, our findings can serve as guidance to the scientific community working in early-stage drug design, food, and environmental chemistry.

Prediction of toluene/water partition coefficients in the SAMPL9 blind challenge: assessment of machine learning and IEF-PCM/MST continuum solvation models.

In recent years the use of partition systems other than the widely used biphasic n-octanol/water has received increased attention to gain insight into the molecular features that dictate the lipophilicity of compounds. Thus, the difference between n-octanol/water and toluene/water partition coefficients has proven to be a valuable descriptor to study the propensity of molecules to form intramolecular hydrogen bonds and exhibit chameleon-like properties that modulate solubility and permeability. In this context, this study reports the experimental toluene/water partition coefficients (log Ptol/w) for a series of 16 drugs that were selected as an external test set in the framework of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) blind challenge. This external set has been used by the computational community to calibrate their methods in the current edition (SAMPL9) of this contest. Furthermore, the study also investigates the performance of two computational strategies for the prediction of log Ptol/w. The first relies on the development of two machine learning (ML) models, which are built up by combining the selection of 11 molecular descriptors in conjunction with either the multiple linear regression (MLR) or the random forest regression (RFR) model to target a dataset of 252 experimental log Ptol/w values. The second consists of the parametrization of the IEF-PCM/MST continuum solvation model from B3LYP/6-31G(d) calculations to predict the solvation free energies of 163 compounds in toluene and benzene. The performance of the ML and IEF-PCM/MST models has been calibrated against external test sets, including the compounds that define the SAMPL9 log Ptol/w challenge. The results are used to discuss the merits and weaknesses of the two computational approaches.

Structural, energetic and lipophilic analysis of SARS-CoV-2 non-structural protein 9 (NSP9).

In SARS-CoV-2 replication complex, the Non-structural protein 9 (Nsp9) is an important RNA binding subunit in the RNA-synthesizing machinery. The dimeric forms of coronavirus Nsp9 increase their nucleic acid binding affinity and the N-finger motif appears to play a critical role in dimerization. Here, we present a structural, lipophilic and energetic study about the Nsp9 dimer of SARS-CoV-2 through computational methods that complement hydrophobicity scales of amino acids with molecular dynamics simulations. Additionally, we presented a virtual N-finger mutation to investigate whether this motif contributes to dimer stability. The results reveal for the native dimer that the N-finger contributes favorably through hydrogen bond interactions and two amino acids bellowing to the hydrophobic region, Leu45 and Leu106, are crucial in the formation of the cavity for potential drug binding. On the other hand, Gly100 and Gly104, are responsible for stabilizing the α-helices and making the dimer interface remain stable in both, native and mutant (without N-finger motif) systems. Besides, clustering results for the native dimer showed accessible cavities to drugs. In addition, the energetic and lipophilic analysis reveal that the higher binding energy in the native dimer can be deduced since it is more lipophilic than the mutant one, increasing non-polar interactions, which is in line with the result of MM-GBSA and SIE approaches where the van der Waals energy term has the greatest weight in the stability of the native dimer. Overall, we provide a detailed study on the Nsp9 dimer of SARS-CoV-2 that may aid in the development of new strategies for the treatment and prevention of COVID-19.

Prediction of n-octanol/water partition coefficients and acidity constants (pKa) in the SAMPL7 blind challenge with the IEFPCM-MST model.

Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pKa were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pKa values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method.

Multiple linear regression models for predicting the n­octanol/water partition coefficients in the SAMPL7 blind challenge.

A multiple linear regression model called MLR-3 is used for predicting the experimental n-octanol/water partition coefficient (log PN) of 22 N-sulfonamides proposed by the organizers of the SAMPL7 blind challenge. The MLR-3 method was trained with 82 molecules including drug-like sulfonamides and small organic molecules, which resembled the main functional groups present in the challenge dataset. Our model, submitted as "TFE-MLR", presented a root-mean-square error of 0.58 and mean absolute error of 0.41 in log P units, accomplishing the highest accuracy, among empirical methods and also in all submissions based on the ranked ones. Overall, the results support the appropriateness of multiple linear regression approach MLR-3 for computing the n-octanol/water partition coefficient in sulfonamide-bearing compounds. In this context, the outstanding performance of empirical methodologies, where 75% of the ranked submissions achieved root-mean-square errors < 1 log P units, support the suitability of these strategies for obtaining accurate and fast predictions of physicochemical properties as partition coefficients of bioorganic compounds.

Prediction of the n-octanol/water partition coefficients in the SAMPL6 blind challenge from MST continuum solvation calculations.

The IEFPCM/MST continuum solvation model is used for the blind prediction of n-octanol/water partition of a set of 11 fragment-like small molecules within the SAMPL6 Part II Partition Coefficient Challenge. The partition coefficient of the neutral species (log P) was determined using an extended parametrization of the B3LYP/6-31G(d) version of the Miertus-Scrocco-Tomasi continuum solvation model in n-octanol. Comparison with the experimental data provided for partition coefficients yielded a root-mean square error (rmse) of 0.78 (log P units), which agrees with the accuracy reported for our method (rmse = 0.80) for nitrogen-containing heterocyclic compounds. Out of the 91 sets of log P values submitted by the participants, our submission is within those with an rmse < 1 and among the four best ranked physical methods. The largest errors involve three compounds: two with the largest positive deviations (SM13 and SM08), and one with the largest negative deviations (SM15). Here we report the potentiometric determination of the log P for SM13, leading to a value of 3.62 ± 0.02, which is in better agreement with most empirical predictions than the experimental value reported in SAMPL6. In addition, further inclusion of several conformations for SM08 significantly improved our results. Inclusion of these refinements led to an overall error of 0.51 (log P units), which supports the reliability of the IEFPCM/MST model for predicting the partitioning of neutral compounds.

Structural and energetic study of cation-π-cation interactions in proteins.

Cation-π interactions of aromatic rings and positively charged groups are among the most important interactions in structural biology. The role and energetic characteristics of these interactions are well established. However, the occurrence of cation-π-cation interactions is an unexpected motif, which raises intriguing questions about its functional role in proteins. We present a statistical analysis of the occurrence, composition and geometrical preferences of cation-π-cation interactions identified in a set of non-redundant protein structures taken from the Protein Data Bank. Our results demonstrate that this structural motif is observed at a small, albeit non-negligible frequency in proteins, and suggest a preference to establish cation-π-cation motifs with Trp, followed by Tyr and Phe. Furthermore, we have found that cation-π-cation interactions tend to be highly conserved, which supports their structural or functional role. Finally, we have performed an energetic analysis of a representative subset of cation-π-cation complexes combining quantum-chemical and continuum solvation calculations. Our results point out that the protein environment can strongly screen the cation-cation repulsion, leading to an attractive interaction in 64% of the complexes analyzed. Together with the high degree of conservation observed, these results suggest a potential stabilizing role in the protein fold, as demonstrated recently for a miniature protein (Craven et al., J. Am. Chem. Soc. 2016, 138, 1543). From a computational point of view, the significant contribution of non-additive three-body terms challenges the suitability of standard additive force fields for describing cation-π-cation motifs in molecular simulations.

Can Förster Theory Describe Stereoselective Energy Transfer Dynamics in a Protein-Ligand Complex?

Förster resonance energy transfer (FRET) reactions involving ligands and aromatic amino acids can substantially impact the fluorescence properties of a protein-ligand complex, an impact intimately related to the corresponding binding mode. Structural characterization of such binding events in terms of intermolecular distances can be done through the well-known R-6 distance-dependent Förster rate expression. However, such an interpretation suffers from uncertainties underlying Förster theory in the description of the electronic coupling that promotes FRET, mostly related to the dipole-dipole orientation factor, dielectric screening effects, and deviations from the ideal dipole approximation. Here, we investigate how Förster approximations impact the prediction of energy transfer dynamics in the complex between flurbiprofen (FBP) and human serum albumin (HSA), as well as a model FBP-Trp dyad, in which recent observation of enantioselective fluorescence quenching has been ascribed to energy transfer from FBP to Trp. To this end, we combine classical molecular dynamics simulations with polarizable quantum mechanics/molecular mechanics calculations that allow overcoming Förster approximations. On the basis of our results, we discuss the potential of structure-based simulations in the characterization of drug-binding events through fluorescence techniques. Overall, we find an excellent agreement between theory and experiment both in terms of enantioselectivity and FRET times, thus strongly supporting the reliability of the binding modes proposed for the (S) and (R) enantiomers of FBP. In particular, we show that the dynamic quenching arises from a small fraction of drug bound to the secondary site of HSA at the interface between subdomains IIA and IIB, whereas the enantioselectivity arises from the larger flexibility of the (S)-FBP enantiomer in the binding pocket.

A utilização dos antiinflamatórios seletivos para COX-2 em Odontologia / The use of antiinflamatory selective to COX-2 in Dentistry

Os antiinflamatórios não-esteróides representam a classe de medicamentos mais amplamente utilizada na área da saúde. Porém, sabe-se que eles podem desencadear efeitos colaterais com relação ao trato gastrointestinal, à função renal e hemostática, dentre outros. Em razão disso, um novo grupo de antiinflamatórios foi criado na tentativa de minimizar esses problemas: os antiinflamatórios (AINEs) seletivos para a COX-2. Sendo assim, o presente trabalho visa, através de uma ampla revisão da literatura, destacar e alertar os profissionais da área da saúde quanto às reais indicações, contra-indicações e efeitos colaterais que os AINEs seletivos para a COX-2 podem causar