Structure‒tissue exposure/selectivity relationship (STR) correlates with clinical efficacy/safety

Exploring the tissue distribution propensity of active alkaloids in normal and stomach heat syndrome rats following oral administration of Zuojin Pill based on pharmacokinetics and mass spectrometry imaging

ETHNOPHARMACOLOGICAL RELEVANCE

Zuojin Pill (ZJP) is a traditional Chinese medicine (TCM) formula composed of Coptidis Rhizoma and Euodiae Fructus in a ratio of 6:1 (w/w), which has been widely used for treating gastrointestinal disorders, especially stomach heat syndrome (SHS). However, the active alkaloids in ZJP showed low plasma exposure in rats following oral administration, which failed to explain their potent pharmacological effects, thereby limiting further mechanism studies.

AIM OF THE STUDY

This study aimed to investigate the in vivo exposure and tissue distribution propensities of the active alkaloids in normal and SHS rats following oral administration of ZJP.

MATERIAL AND METHODS

A rat model of SHS was induced by oral administration of chili pepper decoction and anhydrous ethanol. Then, the plasma and tissue pharmacokinetics of active alkaloids, including four protoberberine alkaloids (PBAs) and three indole alkaloids (IDAs), were investigated following oral administration of ZJP. Furthermore, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was employed to characterize the spatial distribution of active alkaloids in the stomach and liver. Western blot and immunofluorescence were used to evaluate the gastric mucosal barrier integrity.

RESULTS

Based on the tissue-to-plasma partition coefficient (Kp) values, the in vivo exposure levels of berberine (BBR), palmatine (PAL), coptisine (COP), and dehydroevodiamine (DHE) were found to be higher in tissues than in plasma, indicating a distinct tissue distribution propensity. Each alkaloid displayed the highest exposure in the gastrointestinal tissues, due to local penetration facilitated by its direct contact with the mucosal lining. Pathological states reduced the overall exposure of PBAs in the gastric mucosa. In non-gastrointestinal tissues, most alkaloids, especially BBR and COP, exhibited a potent liver distribution propensity with minimal impact from pathological states. According to DESI-MSI results, PBAs showed high exposure in the damaged regions of gastric mucosa, which was attributed to mucosal barrier damage and enhanced permeability. In the liver, PBAs were primarily localized in the parenchyma surrounding the central vein and portal area.

CONCLUSION

This study demonstrated the stomach and liver distribution propensity of the active alkaloids in ZJP, providing a scientific basis for these alkaloids as the pharmacodynamic material basis of ZJP against SHS from the perspective of drug exposure.

A new paradigm for drug discovery in the treatment of complex diseases: drug discovery and optimization

In the past, the drug research and development has predominantly followed a "single target, single disease" model. However, clinical data show that single-target drugs are difficult to interfere with the complete disease network, are prone to develop drug resistance and low safety in clinical use. The proposal of multi-target drug therapy (also known as "cocktail therapy") provides a new approach for drug discovery, which can affect the disease and reduce adverse reactions by regulating multiple targets. Natural products are an important source for multi-target innovative drug development, and more than half of approved small molecule drugs are related to natural products. However, there are many challenges in the development process of natural products, such as active drug screening, target identification and preclinical dosage optimization. Therefore, how to develop multi-target drugs with good drug resistance from natural products has always been a challenge. This article summarizes the applications and shortcomings of related technologies such as natural product bioactivity screening, clarify the mode of action of the drug (direct/indirect target), and preclinical dose optimization. Moreover, in response to the challenges faced by natural products in the development process and the trend of interdisciplinary and multi-technology integration, and a multi-target drug development strategy of "active substances - drug action mode - drug optimization" is proposed to solve the key challenges in the development of natural products from multiple dimensions and levels.

Structure-Tissue Exposure/Selectivity Relationship (STR) on Carbamates of Cannabidiol

The structure-tissue exposure/selectivity relationship (STR) aids in lead optimization to improve drug candidate selection and balance clinical dose, efficacy, and toxicity. In this work, butyrocholinesterase (BuChE)-targeted cannabidiol (CBD) carbamates were used to study the STR in correlation with observed efficacy/toxicity. CBD carbamates with similar structures and same molecular target showed similar/different pharmacokinetics. L2 and L4 had almost same plasma exposure, which was not correlated with their exposure in the brain, while tissue exposure/selectivity was correlated with efficacy/safety. Structural modifications of CBD carbamates not only changed drug plasma exposure, but also altered drug tissue exposure/selectivity. The secondary amine of carbamate can be metabolized into CBD, while the tertiary amine is more stable. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters can be used to predict STR. Therefore, STR can alter drug tissue exposure/selectivity in normal tissues, impacting efficacy/toxicity. The drug optimization process should balance the structure-activity relationship (SAR) and STR of drug candidates for improving clinical trials.

Characterization of active alkaloids and metabolites in rats after oral administration of Zuojin Pill using UHPLC-Q-TOF-MS combined with bioinformatics and molecular docking analyses

Zuojin Pill (ZJP), a traditional Chinese medicine prescription composed of Rhizoma Coptidis and Euodiae Fructus in the ratio of 6:1 (w/w), has been widely used for the treatment of gastric disorders. However, an in-depth understanding of in vivo metabolism and distribution profiles of protoberberine alkaloids (PBAs) and indole alkaloids (IDAs) in ZJP is lacking. In this study, a method using ultra-high performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) was developed to systematically screen the alkaloids and their metabolites in rat plasma and various tissues after oral administration of ZJP. Furthermore, bioinformatics and molecular docking analyses were conducted to elucidate the contribution of the alkaloids and metabolites enriched in the stomach to the therapeutic effect of ZJP on gastritis. A total of 33 compounds, including 7 prototype alkaloids and 26 metabolites, were chemically defined or tentatively identified in this work. The metabolic pathways of PBAs (hydroxylation, oxidation, reduction, demethylation, demethylenation, glucuronide conjugation, sulfate conjugation) and IDAs (hydroxylation, glucuronide conjugation) were revealed. Notably, 7 prototype alkaloids and 18 metabolites were detected in the stomach, indicating their propensity for gastric distribution. These alkaloids and metabolites showed strong affinities with the 7 hub targets associated with gastritis, such as CCR7, CXCR4, IL6, IFNG, CCL2, TNF, and PTPRC, and could be considered the potential active substances of ZJP for treating gastritis. In conclusion, this study clarified the gastric distribution propensity of PBAs and IDAs and their metabolites, as well as their favorable binding interactions with gastritis-related targets, which could provide essential data for the further study of the pharmacodynamic material basis and gastroprotective mechanism of ZJP.

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a "STAR-guided ML system" (structure-tissue/cell selectivity-activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.

In silico off-target profiling for enhanced drug safety assessment

Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.

Progress and application of lung-on-a-chip for lung cancer

Lung cancer is a malignant tumour with the highest incidence and mortality worldwide. Clinically effective therapy strategies are underutilized owing to the lack of efficient models for evaluating drug response. One of the main reasons for failure of anticancer drug therapy is development of drug resistance. Anticancer drugs face severe challenges such as poor biodistribution, restricted solubility, inadequate absorption, and drug accumulation. In recent years, "organ-on-a-chip" platforms, which can directly regulate the microenvironment of biomechanics, biochemistry and pathophysiology, have been developed rapidly and have shown great potential in clinical drug research. Lung-on-a-chip (LOC) is a new 3D model of bionic lungs with physiological functions created by micromachining technology on microfluidic chips. This approach may be able to partially replace animal and 2D cell culture models. To overcome drug resistance, LOC realizes personalized prediction of drug response by simulating the lung-related microenvironment in vitro, significantly enhancing therapeutic effectiveness, bioavailability, and pharmacokinetics while minimizing side effects. In this review, we present an overview of recent advances in the preparation of LOC and contrast it with earlier in vitro models. Finally, we describe recent advances in LOC. The combination of this technology with nanomedicine will provide an accurate and reliable treatment for preclinical evaluation.

Integration of caveolin-mediated cytosolic delivery and enzyme-responsive releasing of squalenoyl nanoparticles enhance the anti-cancer efficacy of chidamide in pancreatic cancer

We explored the potential of overcoming the dense interstitial barrier in pancreatic cancer treatment by enhancing the uptake of hydrophilic chemotherapeutic drugs. In this study, we synthesized the squalenoyl-chidamide prodrug (SQ-CHI), linking lipophilic squalene (SQ) with the hydrophilic antitumor drug chidamide (CHI) through a trypsin-responsive bond. Self-assembled nanoparticles with sigma receptor-bound aminoethyl anisamide (AEAA) modification, forming AEAA-PEG-SQ-CHI NPs (A-C NPs, size 116.6 ± 0.4 nm), and reference nanoparticles without AEAA modification, forming mPEG-SQ-CHI NPs (M-C NPs, size 88.3 ± 0.3 nm), were prepared. A-C NPs exhibited significantly higher in vitro CHI release (74.7 %) in 0.5 % trypsin medium compared to release (20.2 %) in medium without trypsin. In vitro cell uptake assays revealed 3.6 and 2.3times higher permeation of A-C NPs into tumorspheres of PSN-1/HPSC or CFPAC-1/HPSC, respectively, compared to M-C NPs. Following intraperitoneal administration to subcutaneous tumor-bearing nude mice, the A-C NPs group demonstrated significant anti-pancreatic cancer efficacy, inducing cancer cell apoptosis and inhibiting proliferation in vivo. Mechanistic studies revealed that AEAA surface modification on nanoparticles promoted intracellular uptake through caveolin-mediated endocytosis. This nanoparticle system presents a novel therapeutic approach for pancreatic cancer treatment, offering a delivery strategy to enhance efficacy through improved tumor permeation, trypsin-responsive drug release, and specific cell surface receptor-mediated intracellular uptake.

A gastrointestinal locally activating Janus kinase inhibitor to treat ulcerative colitis

In this study, we integrated machine learning (ML), structure-tissue selectivity-activity-relationship (STAR), and wet lab synthesis/testing to design a gastrointestinal (GI) locally activating JAK inhibitor for ulcerative colitis treatment. The JAK inhibitor achieves site-specific efficacy through high local GI tissue selectivity while minimizing the requirement for JAK isoform specificity to reduce systemic toxicity. We used the ML model (CoGT) to classify whether the designed compounds were inhibitors or noninhibitors. Then we used the regression ML model (MTATFP) to predict their IC50 against related JAK isoforms of predicted JAK inhibitors. The ML model predicted MMT3-72, which was retained in the GI tract, to be a weak JAK1 inhibitor, while MMT3-72-M2, which accumulated in only GI tissues, was predicted to be an inhibitor of JAK1/2 and TYK2. ML docking methods were applied to simulate their docking poses in JAK isoforms. Application of these ML models enabled us to limit our synthetic efforts to MMT3-72 and MMT3-72-M2 for subsequent wet lab testing. The kinase assay confirmed MMT3-72 weakly inhibited JAK1, and MMT3-72-M2 inhibited JAK1/2 and TYK2. We found that MMT3-72 accumulated in the GI lumen, but not in GI tissue or plasma, but released MMT3-72-M2 accumulated in colon tissue with minimal exposure in the plasma. MMT3-72 achieved superior efficacy and reduced p-STAT3 in DSS-induced colitis. Overall, the integration of ML, the structure-tissue selectivity-activity-relationship system, and wet lab synthesis/testing could minimize the effort in the optimization of a JAK inhibitor to treat colitis. This site-specific inhibitor reduces systemic toxicity by minimizing the need for JAK isoform specificity.

Optimization of the Prodrug Moiety of Remdesivir to Improve Lung Exposure/Selectivity and Enhance Anti-SARS-CoV-2 Activity

COVID-19 patients with severe symptoms still lack antiviral treatment options. Although remdesivir is the only FDA-approved drug for those patients, its efficacy is limited by premature hydrolysis to nucleoside (NUC), low accumulation in the disease-targeted tissue (lungs), and low antiviral potency. In this study, we synthesized a new series of remdesivir analogues by modifying the ProTide moiety. In comparison with remdesivir, the lead compound MMT5-14 showed 2- to 7-fold higher antiviral activity in four variants of SARS-CoV-2. By reducing premature hydrolysis in hamsters, MMT5-14 increased the prodrug concentration by 200- to 300-fold in the plasma and lungs but also enhanced lung accumulation of the active metabolite triphosphate nucleosides (NTP) by 5-fold. Compared to remdesivir, MMT5-14 also increased the intracellular uptake and activation in lung epithelial cells by 4- to 25-fold. These data suggest that MMT5-14 could be a potential antiviral drug to treat COVID-19 patients with severe symptoms.