Prediction of the Oral Bioavailability Correlation Between Humans and Preclinical Animals

Sublingual permeability of model drugs in New Zealand White Rabbits: In Vitro-In vivo correlation

This study investigated sublingual drug permeation and administration using five model drugs with diverse physicochemical properties, employing New Zealand White Rabbit sublingual mucosa for in vitro experiments and New Zealand White Rabbits for in vivo studies. The research aimed to determine key permeation parameters, specifically permeability and lag time. A strong linear correlation (r = 0.93, n = 5) was established between in vitro permeability and the distribution coefficient of the model drugs at pH 6.8. The study revealed no significant difference between in vitro and in vivo permeability, suggesting that in vitro studies can reliably predict in vivo permeability for these drugs. However, the in vivo lag time was significantly shorter than the in vitro lag time due to the presence of capillaries in the sublingual mucosa, which provided direct access to the systemic circulation and the absence of an aqueous boundary layer.

Employing Hexahydroquinolines as PfCDPK4 Inhibitors to Combat Malaria Transmission: An Advanced Computational Approach

Background

Existing antimalarial drugs primarily target blood-stage parasites, but there is a need for transmission-blocking drugs to combat malaria effectively. Plasmodium falciparum Calcium-dependent Protein Kinase 4 (CDPK4) is a promising target for such drugs. This study employed advanced in silico analyses of hexahydroquinolines (HHQ) derivatives to identify PfCDPK4 inhibitors capable of disrupting malaria transmission. Structure-based virtual screening (SBVS) was employed to discover HHQ derivatives with the highest binding affinities against the 3D structure of PfCDPK4 (PDB 1D: 4QOX).

Methods

Interaction analysis of protein-ligand complexes utilized Discovery Studio Client, while druglikeness and ADMET properties were assessed using SwissADME and pkCSM web servers, respectively. Quantum mechanical calculations of the top hits were conducted using density functional theory (DFT), and GROMACS was employed to perform the molecular dynamics (MD) simulations. Binding free energy was predicted using the MMPBSA.py tool from the AMBER package.

Results

SBVS identified ten best hits possessing docking scores within the range of -11.2 kcal/mol and -10.6 kcal/mol, surpassing the known inhibitor, BKI-1294 (-9.9 kcal/mol). Among these, 4-[4-(Furan-2-carbonyl)piperazin-1-yl]-1-(naphthalen-2-ylmethyl)-2-oxo-4a,5,6,7,8,8a-hexahydroquinoline-3-carbonitrile (PubChem ID: 145784778) exhibited the highest binding affinity (-11.2 kcal/mol) against PfCDPK4.

Conclusion

Comparative analysis of this compound with BKI-1294 using advanced computational approaches demonstrated competitive potential. These findings suggest the potential of 4-[4-(Furan-2-carbonyl)piperazin-1-yl]-1-(naphthalen-2-ylmethyl)-2-oxo-4a,5,6,7,8,8a-hexahydroquinoline-3-carbonitrile as a promising PfCDPK4 inhibitor for disrupting malaria transmission. However, further experimental studies are warranted to validate its efficacy and safety profile.

Construction of An Oral Bioavailability Prediction Model Based on Machine Learning for Evaluating Molecular Modifications

OBJECTIVE

This study aims to explore the impact of ADME on the Oral Bioavailability (OB) of drugs and to construct a machine learning model for OB prediction. The model is then applied to predict the OB of modified berberine and atenolol molecules to obtain structures with higher OB.

METHODS

Initially, a drug OB database was established, and corresponding ADME characteristics were obtained. The relationship between ADME and OB was analyzed using machine learning, with Morgan fingerprints serving as molecular descriptors. Compounds from the database were input into Random Forest, XGBoost, CatBoost, and LightGBM machine learning models to train the OB 7prediction model and evaluate its performance. Subsequently, berberine and atenolol were modified using Chemdraw software with ten different substituents for mono-substitution, and chlorine atoms for a full range of double substitutions. The modified molecular structures were converted into the same format as the training set for OB prediction. The predicted OB values of the modified structures of berberine and atenolol were compared.

RESULTS

An OB database of 386 drugs was obtained. It was found that smaller molecular weight and a higher number of rotatable bonds (ten or less) could potentially lead to higher OB. The four machine learning models were evaluated using MSE, R2 score, MAE, and MFE as metrics, with Random Forest performing the best. The models' predictions for the test set were particularly accurate when OB ranged from 30% to 90%. After mono-substitution and double substitution of berberine and atenolol, the OB of both drugs was significantly improved.

CONCLUSIONS

This study found that some ADME properties of molecules do not have an absolute impact on OB. The database played a decisive role in the process of the machine learning OB prediction model, and the performance of the model was evaluated based on predictions within a range of strong generalization ability. In most cases, mono-substitution and double substitution were beneficial for enhancing the OB of berberine and atenolol. In summary, this study successfully constructed a machine learning regression prediction model that can accurately predict drug OB, which can guide drug design to achieve higher OB to some extent.

Predicting the Drug Clearance Pathway with Structural Descriptors

BACKGROUND AND OBJECTIVE

The clearance, by renal elimination or hepatic metabolism, is one of the most important pharmacokinetic parameters of a drug. It allows the half-life, bioavailability, and drug-drug interactions to be predicted, and it can also affect the dose regimen of a drug. Predicting the clearance pathways of new chemical candidates during drug development is vital in order to minimize the risks of possible side effects and drug interactions. Many in vivo methods have been established to predict drug clearance in humans, and these mainly rely on data from in vivo studies in preclinical species-mainly rats, dogs, and monkeys. They are also time consuming and expensive. The aim of this study was to find the relationship between structural parameters of drugs and their clearance pathways.

METHODS

The clearance pathway of each drug was obtained from the literature. Various structural descriptors [Abraham solvation parameters, topological polar surface area, numbers of hydrogen-bond donors and acceptors, number of rotatable bonds, molecular weight, logarithm of the partition coefficient (logP), and logarithm of the distribution coefficient at pH 7.4 (logD_7.4)] were applied to develop a mechanistic model for predicting clearance pathways.

RESULTS

The results of this study indicate that compounds with logD_7.4 > 1 or with zero or one hydrogen-bond donor undergo hepatic metabolism, whereas the clearance pathway for chemicals with logD_7.4 < - 2 is renal elimination. Furthermore, models established using logistic regression based on five structural parameters for compounds with - 2 < logD_7.4 < 1 could be used in a clearance pathway prediction tool. The overall prediction accuracies of the first and second models were 84.8% and 84.4%, respectively.

CONCLUSION

The developed model can be used to find the clearance pathways of new drug candidates with acceptable accuracy. The main descriptors that are used to evaluate this parameter are the hydrophobicity and the number of hydrogen-bonding functional groups of the compound.

5-Substituted isatin thiosemicarbazones as inhibitors of tyrosinase: Insights of substituent effects

Seven isatin-thiosemicarbazone analogues bearing different substituents (R) attached at C-5 of the indoline ring, TSC-ISA-R (R = -H, -CH₃, -OCH₃, -OCF₃, -F, -Cl and -NO₂), were synthesized and evaluated as inhibitors of mushroom tyrosinase (TYR). The inhibitory behaviour and performance of TSC-ISA-R were investigated spectroscopically in relation to the substituent modifications through examining their inhibition against the diphenolase activity of TYR using L-DOPA as a substrate. The IC₅₀ values of TSC-ISA-R were determined to be in the range of 81-209 μM. The kinetic analysis showed that TSC-ISA-R were reversible and mixed type inhibitors. Three potential non-covalent interactions rather than complexation including the binding of TSC-ISA-R with free TYR, TYR-L-DOPA complex, and with substrate L-DOPA were found to be involved in the inhibition. The substituent modifications affected these interactions by varying the characters of the resulting TSC-ISA-R in different degrees. The thiosemicarbazido moiety of each TSC-ISA-R contributed predominantly to the inhibition, and the isatin moiety seemed to play a regulatory role in the binding of TSC-ISA-R to the target molecules. The results of theoretical calculations using density functional theory method indicated a different effect of -R on the electron distribution in HOMO of TSC-ISA-R. The LUMO-HOMO energy gap of TSC-ISA-R almost accords with the trend of their experimental inhibition potency.

Sub/supercritical Fluid Chromatography Purification and Desalting of a Cyclic Dinucleotide STING Agonist

An efficient and "endotoxin-free" purification of a cyclic dinucleotide (CDN) STING agonist was achieved to produce multigram quantities of pure BMT-390025, an active pharmaceutical ingredient (API), for toxicological studies. A two-step sub/supercritical fluid chromatography (SFC) procedure was developed for the achiral purification and desalting of the polar ionic CDN. A robust SFC process employing methanol-acetonitrile-water with ammonium acetate as co-solvent in CO₂ on BEH 2-ethylpyridine was established and scaled up as the first step to achieve a successful purification. The desalting/salt-switching (i.e. removing acetate and acetamide) was conducted using methanol-water with ammonium hydroxide as co-solvent on the same column in the second step to convert the final API to the ammonium salt. Water with additive was essential to eliminating salt precipitation and improving the peak shape and resolution. Due to the extreme hydrophilicity of BMT-390025, 65% of co-solvent was needed to adequately elute the target in both steps. More than 40 g of crude API was purified and desalted producing >20 g of pure BMT-390025 as the ammonium salt which was obtained with a chemical purity of >98.5% and met the endotoxin requirement of <0.1 EU/mg. In addition, >80 g of its penultimate prior to the deprotection of the silyl group was purified at a high throughput of 6.3 g/h (0.42 g/day/g SP).