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Biotherapeutics are becoming an increasingly common drug class used to treat autoimmune and other inflammatory conditions. Optimization of absorption, distribution, metabolism, and excretion (ADME) profiles of biotherapeutics is crucial for clinical, as well as commercial, success of these drugs. This review focuses on the common questions and challenges in ADME optimization of biotherapeutics for inflammatory conditions. For these immunomodulatory and/or immunosuppressive biotherapeutics, special consideration should be given to the assessment of the interdependency of ADME profiles, pharmacokinetic/pharmacodynamic (PK/PD) relationships, and immunogenicity profiles across various preclinical species and humans, including the interdependencies both in biology and in assay readouts. The context of usage, such as dosing regimens, extent of disease, concomitant medications, and drug product characteristics may have a direct or indirect (via modulation of immunogenicity) impact on ADME profiles of biotherapeutics. Along these lines, emerging topics include assessments of preexisting reactivity to a biotherapeutic agent, impact of immunogenicity on tissue exposure, and analysis of penetration to normal versus inflamed tissues. Because of the above complexities and interdependences, it is essential to interpret PK, PD, and anti-drug antibody results in an integrated manner. In addition, because of the competitive landscape in autoimmune and inflammatory markets, many pioneering ADME-centric protein engineering and subsequent in vivo testing (such as optimization of novel modalities to extend serum and tissue exposures and to improve bioavailability) are being conducted with biotherapeutics in this therapeutic area. However, the ultimate challenge is demonstration of the clinical relevance (or lack thereof) of modified ADME and immunogenicity profiles.

Some new Bis-pyrazoline hybrids 8-17 with dual EGFR and BRAF[sup.V600E] inhibitors have been developed. The target compounds were synthesized and tested in vitro against four cancer cell lines. Compounds 12, 15, and 17 demonstrated strong antiproliferative activity with GI[sub.50] values of 1.05 µM, 1.50 µM, and 1.20 µM, respectively. Hybrids showed dual inhibition of EGFR and BRAF[sup.V600E]. Compounds 12, 15, and 17 inhibited EGFR-like erlotinib and exhibited promising anticancer activity. Compound 12 is the most potent inhibitor of cancer cell proliferation and BRAF[sup.V600E]. Compounds 12 and 17 induced apoptosis by increasing caspase 3, 8, and Bax levels, and resulted in the downregulation of the antiapoptotic Bcl2. The molecular docking studies verified that compounds 12, 15, and 17 have the potential to be dual EGFR/BRAF[sup.V600E] inhibitors. Additionally, in silico ADMET prediction revealed that most synthesized bis-pyrazoline hybrids have low toxicity and adverse effects. DFT studies for the two most active compounds, 12 and 15, were also carried out. The values of the HOMO and LUMO energies, as well as softness and hardness, were computationally investigated using the DFT method. These findings agreed well with those of the in vitro research and molecular docking study.

Metacetamol, a regioisomer of paracetamol known for its significantly lower toxicity, remains largely underexplored in medicinal chemistry despite its potential as a safer therapeutic scaffold. While paracetamol can cause hepatotoxicity through the accumulation of toxic metabolites, metacetamol presents an advantageous starting point for derivatisation and developing new pharmacological agents. This study aimed to synthesise new metacetamol azo derivatives 1–18 and perform structural elucidation with FTIR and NMR spectroscopies. The research further evaluates their antibacterial efficacy through both in vitro assays and in silico modelling. The compounds were produced via a diazocoupling reaction between substituted anilines and metacetamol. Antibacterial potential against Staphylococcus aureus and Escherichia coli was pre-screened using the Kirby-Bauer disc diffusion, followed by the turbidimetric kinetic method for Minimum Inhibitory Concentrations (MIC) determination. Molecular docking was performed against the FtsA enzyme to investigate the mechanism of action, while SwissADME predicted druglikeliness and pharmacokinetic properties. Metacetamol azo 1–18 were successfully synthesised with yields ranging from 32% to 95%. Most derivatives exhibited notable antibacterial inhibition compared to the parent metacetamol, which showed no activity. Specifically, compound 2 (m-F) demonstrated the highest activity against E. coli (MIC = 108.67 ppm), comparable to the reference drug, ampicillin (MIC = 98.79 ppm). All derivatives were found to comply with Lipinski’s Rule of Five, suggesting high oral bioavailability and favourable GI absorption. The enhanced potency of compound 2 (m-F) is attributed to the high electronegativity, specific meta-positional effect and small atomic size of the fluorine atom. These factors minimise steric hindrance while strengthening the C-F bond against metabolic transformation. Molecular docking revealed that 2 (m-F) establishes stable hydrogen bonds with key residues of LYS17 and LYS254 in the FtsA enzyme, potentially inhibiting bacterial cell division. Furthermore, the azo linker improves molecular conjugation and binding affinity, while the secondary phenyl ring facilitates critical interactions with bacterial proteins through π-π stacking and hydrophobic forces. Collectively, the strategic incorporation of an azo linker into metacetamol scaffold significantly elevates antibacterial activity while preserving desirable pharmacokinetic characteristics. These findings provide a promising starting point for optimising metacetamol-based agents as potential therapeutic candidates. [Display omitted]

The aim of this project is to synthesize Sorafenib two derivatives of 2-amino-6-phenoxyquinolone: AH1 & P64, then conduct ADME studies in healthy rats and correlate results with in vitro and in silico results. The absolute bioavailability of sorafenib derivatives were found very low 2.2 & 12 % for AH1 & P64 after in vivo oral and IV studies of the derivatives. Also, the relative bioavailability of sorafenib derivatives were found very low 0.3 & 0.6 % for AH1 & P64 after in vivo studies of sorafenib and its derivatives. In vitro stability tests showed stable derivatives in all degradation tests over the time course of the experiments which suggests stable derivatives in vivo too. However, in vitro diffusion study showed that derivatives permeability values are more than 60 times lower than sorafenib permeability which explains the low bioavailability of the derivatives as compared with sorafenib. Sorafenib derivatives were shown to have more in vitro anti-cancer activity, yet low in vivo bioavailability due to low intestinal permeability.

The bioavailability of small-molecule drugs remains a critical challenge in pharmaceutical development, significantly impacting therapeutic efficacy and commercial viability. This review synthesizes recent advances in understanding and overcoming bioavailability limitations, focusing on key physicochemical and biological factors influencing drug absorption and distribution. We examine cutting-edge strategies for enhancing bioavailability, including innovative formulation approaches, rational structural modifications, and the application of artificial intelligence in drug design. The integration of nanotechnology, 3D printing, and stimuli-responsive delivery systems are highlighted as promising avenues for improving drug delivery. We discuss the importance of a holistic, multidisciplinary approach to bioavailability optimization, emphasizing early-stage consideration of ADME properties and the need for patient-centric design. This review also explores emerging technologies such as CRISPR-Cas9-mediated personalization and microbiome modulation for tailored bioavailability enhancement. Finally, we outline future research directions, including advanced predictive modeling, overcoming biological barriers, and addressing the challenges of emerging therapeutic modalities. By elucidating the complex interplay of factors affecting bioavailability, this review aims to guide future efforts in developing more effective and accessible small-molecule therapeutics.

Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.

Crescentia cujete is widely known as a medical plant with broad indigenous ethnomedicinal uses, including anti-inflammatory, and antioxidant. Despite being used for remedies and ethnomedicinal purposes, the benefits obtained from C. cujete still need to be fully utilized. The underwhelming studies on its pharmacological potential, bioactive compounds, and mechanism of action keep the pharmacological and new drug discovery progress of this plant slow. This study focuses on the incorporation of in silico analyses such as ADME prediction and molecular docking simulations on the bioactive compounds identified in the plant to assess their potential for antioxidant and anti-inflammatory applications. A comparison of the ADME properties and molecular docking scores showed that naringenin, pinocembrin, and eriodictyol had the most potential to act as inhibitors of the target proteins involved in inflammation and oxidation pathways against the positive controls.

In recent times, interest in the chemistry of conjugated nitrodienes is still significantly increasing. In particular, the application of these compounds as building blocks to obtain heterocycles is a popular object of research. Therefore, in continuation of our research devoted to the topic of conjugated nitrodienes, experimental and quantum-chemical studies of a cycloaddition reaction between (1E,3E)-1,4-dinitro-1,3-butadiene and N-methyl azomethine ylide have been investigated. The computational results present that the tested reaction is realized through a pdr-type polar mechanism. In turn, the experimental study shows that in a course of this cycloaddition, only one reaction product in the form of 1-methyl-3-(trans-2-nitrovinyl)-Δ3-pyrroline is created. The constitution of this compound has been confirmed via spectroscopic methods. Finally, ADME analysis indicated that the synthesized Δ3-pyrroline exhibits biological potential, and it is a good drug candidate according to Lipinski, Veber and Egan rules. Nevertheless, PASS simulation showed that the compound exhibits weak antimicrobial, inhibitory and antagonist properties. Preliminary in silico research shows that although the obtained Δ3-pyrroline is not a good candidate for a drug, the presence of a nitrovinyl moiety in its structure indicates that the compound is an initial basis for further modifications.

Cancer chemotherapy resistance (MDR) is the innate and/or acquired ability of cancer cells to evade the effects of chemotherapeutics and is one of the most pressing major dilemmas in cancer therapy. Chemotherapy resistance can arise due to several host or tumor-related factors. However, most current research is focused on tumor-specific factors and specifically genes that handle expression of pumps that efflux accumulated drugs inside malignantly transformed types of cells. In this work, we suggest a wider and alternative perspective that sets the stage for a future platform in modifying drug resistance with respect to the treatment of cancer.

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. For all 13 data sets, Chemi-Net resulted in higher R2 values compared with the Cubist benchmark. The median R2 increase rate over Cubist was 26.7%. We expect that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.