Why 90% of clinical drug development fails and how to improve it?

Drug-delivery and biological activity in colorectal cancer of a supramolecular porous material assembled from heptameric chromium-copper-adenine entities

The therapeutic application of drugs often faces challenges due to non-specific distribution, inadequate dosification and degradation, which limits their efficacy. Two primary strategies are employed to overcome these issues: the use of derivatives of the active substances and incorporation of those into porous materials. The latter, involving materials such as zeolites, metal-organic frameworks (MOFs), and hydrogels, has shown promising results in protecting the active ingredients from degradation and enabling a controlled release. This study focuses on supramolecular metal-organic frameworks (SMOFs), which leverage supramolecular interactions for enhanced pore size control. [Cu6Cr(μ-adeninato-κN3:κN9)6(μ3-OH)6(μ-OH2)6](SO4)1.5·nH2O (Cu6Cr) was chosen for its flexible porous structure, water-stability, and paramagnetic properties. Magnetic sustentation studies showed that this compound was able to capture several drug molecules: 5-fluorouracil (5-FU), 5-aminosalicylic acid (5-ASA), 4-aminosalicylic acid (4-ASA) and theophylline (THEO). Their release follows a pseudo-first-order kinetics with desorption half-lives ranging from 2.2 to 4.7 hours. In this sense, a novel approach is proposed using bulkier raffinose and cholesterol as pore-blocking molecules. Cholesterol exhibited the best performance as a blocking molecule increasing the desorption half-life up to 8.2 hours. Cytotoxicity and RNA-seq transcriptomic assays carried out on human colorectal cancer cell cultures showed, on one hand, that the Cu6Cr porous material exhibits a proliferative effect, probably coming from the over-expression of MIR1248 and SUMO2 genes, and on the other hand, that there is a delay in the emergence of the cytotoxicity of 5-FU as expected for a slower release.

Public attitudes toward the use of human induced pluripotent stem cells: insights from an Italian adult population

Introduction

Human induced pluripotent stem cells (hiPSCs), derived from reprogrammed adult somatic cells, hold significant promise for disease modelling, personalized medicine, drug discovery, and regenerative therapies. Public awareness and understanding of hiPSCs are crucial for advancing research in this field. However, limited data exists on the general population's knowledge and attitudes toward their use.

Methods

This study aimed to assess the awareness and perceptions of hiPSCs among Italian adults through a web-based survey conducted via the EUSurvey platform, using a snowball sampling approach. The survey included demographic information and mandatory questions on knowledge, awareness, and concerns regarding hiPSC technology, with responses collected on a 3-point scale. Statistical analysis was performed using chi-squared tests, with significance set at p ≤ 0.05.

Results

Out of 1874 respondents, the majority were aged 18-35 years (40.5%), female (63.4%), and university-educated (67.2%). Among those familiar with hiPSCs (54.1%, n = 1,201), 95.3% expressed willingness to donate blood samples for hiPSC generation to treat individuals with incurable diseases. Concerns about current research and therapeutic applications were low (less than 20%), but nearly half of the respondents were hesitant or opposed to the use of hiPSCs in animal experiments and their commercialization by pharmaceutical companies. Increased skepticism was observed in older, less educated, religious individuals, and those who were not blood donors. Overall, the Italian public shows strong support for hiPSC-based therapies, though reservations exist around specific ethical and economic issues.

Discussion

These findings underscore the importance of addressing public concerns through targeted educational campaigns, not only in Italy but globally, to foster a more informed and supportive environment for advancing stem cell research and its clinical applications worldwide. Similar studies have been conducted in Japan, the United States, and Sweden, but there remains a need for all countries to engage with their citizens to better understand how stem cell research is perceived locally. Such engagement is crucial for guiding international strategies in personalized medicine and regenerative therapies, ensuring that emerging technologies are met with both ethical integrity and public trust.

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.

Harnessing Artificial Intelligence for the Detection and Management of Colorectal Cancer Treatment

Currently, eight million people in the United States suffer from cancer and it is a major global health concern. Early detection and interventions are urgently needed for all cancers, including colorectal cancer. Colorectal cancer is the third most common type of cancer worldwide. Based on the diagnostic efforts to general awareness and lifestyle choices, it is understandable why colorectal cancer is so prevalent today. There is a notable lack of awareness concerning the impact of this cancer and its connection to lifestyle elements, as well as people sometimes mistaking symptoms for a different gastrointestinal condition. Artificial intelligence (AI) may assist in the early detection of all cancers, including colorectal cancer. The usage of AI has exponentially grown in healthcare through extensive research, and since clinical implementation, it has succeeded in improving patient lifestyles, modernizing diagnostic processes, and innovating current treatment strategies. Numerous challenges arise for patients with colorectal cancer and oncologists alike during treatment. For initial screening phases, conventional methods often result in misdiagnosis. Moreover, after detection, determining the course of which colorectal cancer can sometimes contribute to treatment delays. This article touches on recent advancements in AI and its clinical application while shedding light on why this disease is so common today.

Drug pricing models, no 'one-size-fits-all' approach: a systematic review and critical evaluation of pricing models in an evolving pharmaceutical landscape

Access to new medicines is crucial for patients but increasingly sparks discussion due to high prices. Simultaneously, the growing emphasis on specialized products and uncertainty surrounding the long-term effectiveness of new drug classes brought to the market underscore the need for innovative pricing approaches. A systematic literature review of pharmaceutical pricing models, accompanied by a critical appraisal, was conducted to offer insights contributing to novel approaches balancing sustainable pharmaceutical innovation with affordability and accessibility for patients. Six different pricing models are identified: value based pricing, basic cost-based pricing, and four more comprehensive pricing models incorporating numerous elements: the cancer-drug-pricing model, AIM model, (Nuijtens) discounted cash flow, and the real-option rate of return method. Although there are many similarities among the models, each has unique assumptions for implementation. For instance, all models except for the standard incremental cost-effectiveness ratio and basic cost-based pricing consider the number of eligible patients and the remaining patent period. Only the AIM model and the Nuijtens discounted cash flow model use lump sums. Both the latter and the real-option rate of return method explicitly include the cost of capital as a major cost-based component. Recognizing the diverse applications of each model highlights the need for more differential and dynamic pricing tailored to the characteristics and therapeutic areas of each drug. Additionally, the study underscores the importance of cost transparency in achieving this goal. Consequently, these findings can help stakeholders develop sustainable and affordable drug pricing mechanisms that address the complexities of the ever-changing pharmaceutical landscape.

Impact of Reductive Stress on Human Infertility: Underlying Mechanisms and Perspectives

Antioxidants have a well-established effect on general health and are essential in preventing oxidative damage to cells by scavenging free radicals. Free radicals are thought to be neutralized by these substances, which include polyphenols, β-carotene, and vitamins C and E, reducing cellular damage. On the other hand, recent data indicates that consuming excessive amounts of antioxidants may have side effects. Apoptosis and cell signaling are two beneficial physiological processes that are affected by excessive supplementation. Other negative effects include paradoxical enhancement of oxidative stress and unbalanced cellular redox potential. Overdosing on particular antioxidants has been associated with increased medication interactions, cancer progression, and fatality risks. Additionally, the complex impacts they may have on fertility might be both useful and adverse, depending on the quantity and duration of usage. This review delves into the dual role of antioxidants and emphasizes the importance of employing antioxidants in moderation. Antioxidant overconsumption may disrupt the oxidative balance necessary for normal sperm and oocyte function, which is one of the potential negative effects of antioxidants on fertility in both males and females that are also investigated. Although modest usage of antioxidants is generally safe and useful, high levels of antioxidants can upset hormonal balance, impair sperm motility, and negatively impact the outcomes of assisted reproductive technologies (ART). The findings emphasize the need to use antioxidant supplements in a balanced way, the importance of further research to optimize their use in fertility treatments, and the importance of supporting reproductive health to avoid adverse effects.

When Non-Promising Findings Also Go From Aquarium to Bedside: The Importance of Rigorous Preclinical Analyses of ASM Candidates

Testing of Putative Antiseizure Medications in a Preclinical Dravet Syndrome Zebrafish Model Whyte-Fagundes PA, Vance A, Carroll A, Figueroa F, Manukyan C, Baraban SC. Brain Commun . 2024;6(3):fcae135. Dravet syndrome is a severe genetic epilepsy primarily caused by de novo mutations in a voltage-activated sodium channel gene (SCN1A). Patients face life-threatening seizures that are largely resistant to available anti-seizure medications. Preclinical Dravet syndrome animal models are a valuable tool to identify candidate anti-seizure medications for these patients. Among these, scn1lab mutant zebrafish, exhibiting spontaneous seizure-like activity, are particularly amenable to large-scale drug screening. Thus far, we have screened more than 3000 drug candidates in scn1lab zebrafish mutants, identifying valproate, stiripentol, and fenfluramine e.g. Food and Drug Administration-approved drugs, with clinical application in the Dravet syndrome population. Successful phenotypic screening in scn1lab mutant zebrafish is rigorous and consists of two stages: (i) a locomotion-based assay measuring high-velocity convulsive swim behaviour and (ii) an electrophysiology-based assay, using in vivo local field potential recordings, to quantify electrographic seizure-like events. Historically, nearly 90% of drug candidates fail during translation from preclinical models to the clinic. With such a high failure rate, it becomes necessary to address issues of replication and false positive identification. Leveraging our scn1lab zebrafish assays is one approach to address these problems. Here, we curated a list of nine anti-seizure drug candidates recently identified by other groups using preclinical Dravet syndrome models: 1-Ethyl-2-benzimidazolinone, AA43279, chlorzoxazone, donepezil, lisuride, mifepristone, pargyline, soticlestat and vorinostat. First-stage locomotion-based assays in scn1lab mutant zebrafish identified only 1-Ethyl-2-benzimidazolinone, chlorzoxazone and lisuride. However, second-stage local field potential recording assays did not show significant suppression of spontaneous electrographic seizure activity for any of the nine anti-seizure drug candidates. Surprisingly, soticlestat induced frank electrographic seizure-like discharges in wild-type control zebrafish. Taken together, our results failed to replicate clear anti-seizure efficacy for these drug candidates highlighting a necessity for strict scientific standards in preclinical identification of anti-seizure medications.

Targeted Cancer Therapy-on-A-Chip

Targeted cancer therapy (TCT) is gaining increased interest because it reduces the risks of adverse side effects by specifically treating tumor cells. TCT testing has traditionally been performed using two-dimensional (2D) cell culture and animal studies. Organ-on-a-chip (OoC) platforms have been developed to recapitulate cancer in vitro, as cancer-on-a-chip (CoC), and used for chemotherapeutics development and testing. This review explores the use of CoCs to both develop and test TCTs, with a focus on three main aspects, the use of CoCs to identify target biomarkers for TCT development, the use of CoCs to test free, un-encapsulated TCTs, and the use of CoCs to test encapsulated TCTs. Despite current challenges such as system scaling, and testing externally triggered TCTs, TCToC shows a promising future to serve as a supportive, pre-clinical platform to expedite TCT development and bench-to-bedside translation.

Enhanced drug classification using machine learning with multiplexed cardiac contractility assays

Cardiac screening of newly discovered drugs remains a longstanding challenge for the pharmaceutical industry. While therapeutic efficacy and cardiotoxicity are evaluated through preclinical biochemical and animal testing, 90 % of lead compounds fail to meet safety and efficacy benchmarks during human clinical trials. A preclinical model more representative of the human cardiac response is needed; heart tissue engineered from human pluripotent stem cell derived cardiomyocytes offers such a platform. In this study, three functionally distinct and independently validated engineered cardiac tissue assays are exposed to increasing concentrations of known compounds representing 5 classes of mechanistic action, creating a robust electrophysiology and contractility dataset. Combining results from six individual models, the resulting ensemble algorithm can classify the mechanistic action of unknown compounds with 86.2 % predictive accuracy. This outperforms single-assay models and offers a strategy to enhance future clinical trial success aligned with the recent FDA Modernization Act 2.0.

Solubilization techniques used for poorly water-soluble drugs

About 40% of approved drugs and nearly 90% of drug candidates are poorly water-soluble drugs. Low solubility reduces the drugability. Effectively improving the solubility and bioavailability of poorly water-soluble drugs is a critical issue that needs to be urgently addressed in drug development and application. This review briefly introduces the conventional solubilization techniques such as solubilizers, hydrotropes, cosolvents, prodrugs, salt modification, micronization, cyclodextrin inclusion, solid dispersions, and details the crystallization strategies, ionic liquids, and polymer-based, lipid-based, and inorganic-based carriers in improving solubility and bioavailability. Some of the most commonly used approved carrier materials for solubilization techniques are presented. Several approved poorly water-soluble drugs using solubilization techniques are summarized. Furthermore, this review summarizes the solubilization mechanism of each solubilization technique, reviews the latest research advances and challenges, and evaluates the potential for clinical translation. This review could guide the selection of a solubilization approach, dosage form, and administration route for poorly water-soluble drugs. Moreover, we discuss several promising solubilization techniques attracting increasing attention worldwide.

Schiff Base Mediated Synthesis of Novel Imidazolidine-4-One Derivatives for Potential Antimicrobial and Anthelmintic Activities

This study focuses on developing novel antimicrobials to combat drug-resistant pathogens, addressing compounds failing clinical trials due to inadequate physicochemical properties. Sixteen imidazolidine-4-one derivatives were synthesized by extensive evaluation using molecular docking, absorption, distribution, metabolism, excretion (ADME) predictions, and antimicrobial testing. Molecular docking studies conducted with Schrödinger's Glide revealed that compounds S4 and G8 exhibited superior docking scores of -7.839 and -7.776, respectively. The G series outperformed the S series in scores. ADME analysis confirmed all compounds adhered to Lipinski's rule of five. In addition, IR and NMR provided details about the structure of the compounds. Antimicrobial activity was assessed against Escherichia coli, Staphylococcus aureus, and Candida albicans, with compounds G2 and S2 showing exceptional minimum inhibitory concentration (MIC) values of 6.25 μg/mL against E. coli. S2 also demonstrated impressive activity against S. aureus (MIC 3.12 μg/mL), and S4 exhibited potent activity against C. albicans (MIC 0.8 μg/mL) than fluconazole (1.6 μg/mL). Additionally, antihelmintic activity was evaluated, with G1, G3, G8, S2, S4, S7, and S8 showing effective paralysis and death time 20 min and below at 50 mg/mL concentration. These results underscore the potential of new imidazolidine-4-one derivatives as suitable sources to develop a drug candidate to treat resistant infections.

The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?

Over the last four decades, pharmaceutical companies' expenditures on research and development have increased 51-fold. During this same time, clinical success rates for new drugs have remained unchanged at about 10 percent, predominantly due to lack of efficacy and/or safety concerns. This persistent problem underscores the need to innovate across the entire drug development process, particularly in drug formulation, which is often deprioritized and under-resourced.

3D Humanized Bioprinted Tubulointerstitium Model to Emulate Renal Fibrosis In Vitro

Chronic kidney disease (CKD) leads to a gradual loss of kidney function, with fibrosis as pathological endpoint, which is characterized by extracellular matrix (ECM) deposition and remodeling. Traditionally, in vivo models are used to study interstitial fibrosis, through histological characterization of biopsy tissue. However, ethical considerations and the 3Rs (replacement, reduction, and refinement) regulations emphasizes the need for humanized 3D in vitro models. This study introduces a bioprinted in vitro model which combines primary human cells and decellularized and partially digested extracellular matrix (ddECM). A protocol was established to decellularize kidney pig tissue and the ddECM was used to encapsulate human renal cells. To investigate fibrosis progression, cells were treated with transforming growth factor beta 1 (TGF-β1), and the mechanical properties of the ddECM hydrogel were modulated using vitamin B2 crosslinking. The bioprinting perfusable model replicates the renal tubulointerstitium. Results show an increased Young's modulus over time, together with the increase of ECM components and cell dedifferentiation toward myofibroblasts. Multiple fibrotic genes resulted upregulated, and the model closely resembled fibrotic human tissue in terms of collagen deposition. This 3D bioprinted model offers a more physiologically relevant platform for studying kidney fibrosis, potentially improving disease progression research and high-throughput drug screening.

Neonatal encephalopathy due to suspected hypoxic ischemic encephalopathy: pathophysiology, current, and emerging treatments

BACKGROUND

Neonatal encephalopathy (NE) due to suspected hypoxic-ischemic encephalopathy (HIE), referred to as NESHIE, is a clinical diagnosis in late preterm and term newborns. It occurs as a result of impaired cerebral blood flow and oxygen delivery during the peripartum period and is used until other causes of NE have been discounted and HIE is confirmed. Therapeutic hypothermia (TH) is the only evidence-based and clinically approved treatment modality for HIE. However, the limited efficacy and uncertain benefits of TH in some low- to middle-income countries (LMICs) and the associated need for intensive monitoring have prompted investigations into more accessible and effective stand-alone or additive treatment options.

DATA SOURCES

This review describes the rationale and current evidence for alternative treatments in the context of the pathophysiology of HIE based on literatures from Pubmed and other online sources of published data.

RESULTS

The underlining mechanisms of neurotoxic effect, current clinically approved treatment, various categories of emerging treatments and clinical trials for NE are summarized in this review. Melatonin, caffeine citrate, autologous cord blood stem cells, Epoetin alfa and Allopurinal are being tested as potential neuroprotective agents currently.

CONCLUSION

This review describes the rationale and current evidence for alternative treatments in the context of the pathophysiology of HIE. Neuroprotective agents are currently only being investigated in high- and middle-income settings. Results from these trials will need to be interpreted and validated in LMIC settings. The focus of future research should therefore be on the development of inexpensive, accessible monotherapies and should include LMICs, where the highest burden of NESHIE exists.

Membrane-active peptides for anticancer therapies

Membrane-active peptides are found in many living organisms and play a critical role in their immune systems by combating various infectious diseases. These host defense peptides employ multiple mechanisms against different microorganisms and possess unique functions, such as anti-inflammatory and immunomodulatory effects, often working in synergy with other antimicrobial agents. Despite extensive research over the past few decades and the identification of thousands of sequences, only a few have been successfully applied in clinical settings and received approval from the U.S. Food and Drug Administration. In this chapter, we explore all peptide therapeutics that have reached the market, as well as candidates in preclinical and clinical trials, to understand their success and potential applications in cancer therapy. Our findings indicate that at least four membrane-active peptide drugs have progressed to preclinical or clinical phases, dmonstrating promising results for cancer treatment. We summarize our insights in this chapter, highlighting the potential of membrane-active anticancer peptide therapeutics and their applications as targeting ligands in various biomedical fields.

In Silico Approach for Assessment of the Anti-Tumor Potential of Cannabinoid Compounds by Targeting Glucose-6-Phosphate Dehydrogenase Enzyme

Glucose-6-phosphate dehydrogenase (G6PD) is a pentose phosphate pathway (PPP) enzyme that generates NADPH, which is required for cellular redox equilibrium and reductive biosynthesis. It has been demonstrated that abnormal G6PD activation promotes cancer cell proliferation and metastasis. To date, no G6PD inhibitor has passed clinical testing successfully enough to be launched as a medicine. As a result, in this investigation, cannabinoids were chosen to evaluate their anticancer potential by targeting G6PD. Molecular docking indicated that three molecules, Tetrahydrocannabinolic acid (THCA), Cannabichromenic acid (CBCA), and tetrahydrocannabivarin (THCV), have the highest binding affinities for G6PD of -8.61, -8.39, and 8.01 Kcal mol. ADMET analysis found that all of them were safe prospective drug candidates. Molecular dynamics (MD) simulation and MM-PBSA analysis confirm the structural compactness and lower conformational variation of protein-ligand complexes, thereby maintaining structural stability and rigidity. Thus, our in silico investigation exhibited all three cannabinoids as potential competitive inhibitors of G6PD.

Harms with placebo in trials of biological therapies and small molecules as induction therapy in inflammatory bowel disease: a systematic review and meta-analysis

BACKGROUND

Randomised placebo-controlled trials are the gold standard to assess novel drugs in ulcerative colitis and Crohn's disease. However, there might be risks associated with receiving placebo. We aimed to examine the harms associated with receiving placebo in trials of licensed biologics and small molecules for the induction of remission in ulcerative colitis and luminal Crohn's disease in a meta-analysis.

METHODS

We performed a systematic review and meta-analysis. We searched MEDLINE, Embase, Embase Classic, and the Cochrane Central Register of Controlled Trials from database inception to May 30, 2024, for randomised placebo-controlled trials of licensed biologics and small molecules for induction of remission in adults (≥18 years) with moderately to severely active ulcerative colitis or luminal Crohn's disease reporting data on adverse events over a minimum treatment period of 4 weeks. There were no prespecified study exclusion criteria. We extracted summary data and pooled data using a random-effects model for any treatment-emergent adverse event, any drug-related adverse event, infection, worsening of inflammatory bowel disease (IBD) activity, withdrawal due to adverse events, serious adverse events, serious infection, serious worsening of IBD activity, or venous thromboembolic events (VTEs), reporting relative risks (RRs) with 95% CIs. The protocol for this meta-analysis was registered with PROSPERO (CRD42024527341).

FINDINGS

The search identified 10 826 citations, of which 47 trials including 20 987 patients (14 267 [68·0%] receiving active drug and 6720 [32·0%] receiving placebo) were eligible. The risk of any treatment-emergent adverse event was no different with active drug than with placebo (7660/14 267 [53·7%] patients on active drug vs 3758/6720 [55·9%] on placebo; RR 0·97, 95% CI 0·94-1·00; I2 =36%). However, the risks of worsening of IBD activity (563/13 473 [4·2%] vs 530/6252 [8·5%]; 0·48, 0·40-0·59; I2 =54%), withdrawal due to adverse event (401/13 363 [3·0%] vs 299/6267 [4·8%]; 0·62, 0·48-0·79; I2 =46%), serious adverse event (682/14 267 [4·8%] vs 483/6720 [7·2%]; 0·69, 0·59-0·80; I2 =30%), serious infection (140/14 194 [1·0%] vs 91/6647 [1·4%]; 0·67, 0·50-0·89; I2 =0%), serious worsening of IBD activity (187/11 271 [1·7%] vs 189/5056 [3·7%]; 0·45, 0·34-0·60; I2 =27%), or VTEs (13/7542 [0·2%] vs 12/2981 [0·4%]; 0·45, 0·21-0·94; I2 =0%) were all significantly lower with active drug than placebo. Numbers needed to treat with active drug to avoid these potentially serious adverse events ranged from 23 for worsening of IBD activity to 452 for VTEs. 27 randomised controlled trials were judged as low risk of bias across all domains.

INTERPRETATION

Patients with moderately to severely active IBD receiving placebo are more likely to experience significant worsening of IBD activity and some serious adverse events, which might relate to a reduction in risk of these events with active drug. Patients should be counselled about these potential harms, and alternative trial designs to mitigate these harms should be considered.

FUNDING

None.

GDNF and cAMP significantly enhance in vitro blood-brain barrier integrity in a humanized tricellular transwell model

Blood-brain barrier (BBB) is a crucial membrane safeguarding neural tissue by controlling the molecular exchange between blood and the brain. However, assessing BBB permeability presents challenges for central nervous system (CNS) drug development. In vitro studies of BBB-permeable agents before animal testing are essential to mitigate failures. Improved in vitro models are needed to mimic physiologically relevant BBB integrity. Here, we established an in vitro human-derived triculture BBB model, coculturing hCMEC/D3 with primary astrocytes and pericytes in a transwell format. This study found that the triculture BBB model exhibited significantly higher paracellular tightness (TEER 147.6 ± 6.5 Ω × cm2) than its monoculture counterpart (106.3 ± 1.0 Ω × cm2). Additionally, BBB permeability in the triculture model was significantly lower. While GDNF and cAMP have been shown to promote BBB integrity in monoculture models, their effect in our model was previously unreported. Our study demonstrates that both GDNF and cAMP increased TEER values (around 200 Ω × cm2 for each; 237.6 ± 17.7 Ω × cm2 for co-treatment) compared to untreated control, and decreased BBB permeability, mediated by increased claudin-5 expression. In summary, this humanized triculture BBB model, enhanced by GDNF and cAMP, offers an alternative for exploring in vitro drug penetration into the human brain.

Prospecting Pharmacologically Active Biocompounds from the Amazon Rainforest: In Vitro Approaches, Mechanisms of Action Based on Chemical Structure, and Perspectives on Human Therapeutic Use

The Amazon rainforest is an important reservoir of biodiversity, offering vast potential for the discovery of new bioactive compounds from plants. In vitro studies allow for the investigation of biological processes and interventions in a controlled manner, making them fundamental for pharmacological and biotechnological research. These approaches are faster and less costly than in vivo studies, providing standardized conditions that enhance the reproducibility and precision of data. However, in vitro methods have limitations, including the inability to fully replicate the complexity of a living organism and the absence of a complete physiological context. Translating results to in vivo models is not always straightforward, due to differences in pharmacokinetics and biological interactions. In this context, the aim of this literature review is to assess the advantages and disadvantages of in vitro approaches in the search for new drugs from the Amazon, identifying the challenges and limitations associated with these methods and comparing them with in vivo testing. Thus, bioprospecting in the Amazon involves evaluating plant extracts through bioassays to investigate pharmacological, antimicrobial, and anticancer activities. Phenolic compounds and terpenes are frequently identified as the main bioactive agents, exhibiting antioxidant, anti-inflammatory, and antineoplastic activities. Chemical characterization, molecular modifications, and the development of delivery systems, such as nanoparticles, are highlighted to improve therapeutic efficacy. Therefore, the Amazon rainforest offers great potential for the discovery of new drugs; however, significant challenges, such as the standardization of extraction methods and the need for in vivo studies and clinical trials, must be overcome for these compounds to become viable medications.

Exploring the Impact of Pharmacological Target-Mediated Low Plasma Exposure in Lead Compound Selection in Drug Discovery - A Modeling Approach

Small-molecule drug development faces the challenge of low success rate. In this paper, we propose one potential cause that may occur in the preclinical phase and has rarely been brought up before - the neglected target-mediated low plasma exposure, and the subsequent lead compound mis-selection due to conventional pharmacokinetic criteria requiring sufficient plasma exposure and desired half-life. To evaluate the concept of target-mediate low plasma exposure, we established a minimal physiologically-based pharmacokinetic (mPBPK) model to evaluate the concentration-time profiles of a group of virtual lead series analogs in plasma and in tissues with and without pharmacological target expression. Simulation results demonstrated that the candidate with the highest target binding has the lowest plasma exposure due to target-mediated tissue retention. The traditional PK criteria, such as the requirement of sufficient plasma exposure and desired half-life, may potentially result in lead compound mis-selection by discarding the appropriate and best candidate(s). The mPBPK model was partially validated using 4 tyrosine kinase inhibitors based on our in-house PK and tissue distribution data obtained in animals. The association rate constant (Kass) was estimated to be 49.8 h-1, 31.4 h-1, 8.58 h-1, and 1.91 h-1 for afatinib, dasatinib, gefitinib, and sorafenib, respectively. Among these four model drugs, a strong correlation was observed between their Kass values and AUChigh-perfused tissue /AUCplasma ratios, a metric of tissue retention. Our mPBPK modeling and simulation results indicated that the concept of target-mediated low plasma exposure should be kept in mind during the lead compound selection process.

Epoxy-a-lapachone in nanosystem: a prototype drug for leishmaniasis assessed in the binomial BALB/c - Leishmania (Leishmania) amazonensis

This perspective presents and supports arguments for a new formulation of epoxy-α-lapachone loaded microemulsion (ELAP-ME), a nanosystem, as a prototype drug for the treatment of leishmaniasis. The benefits of ELAP as a multitarget compound, with properties that affect key physiological pathways of Leishmania spp. are discussed. ELAP-ME demonstrated efficacy in murine infection models, particularly with the binomial BALB/c-Leishmania (Leishmania) amazonensis. Furthermore, it is proposed that the technological maturity of ELAP-ME be classified as Technology Readiness Level 4 (TLR 4) within the context of innovative drugs for American Cutaneous Leishmaniasis (ACL).

Discovery of novel CDK4/6 inhibitors from fungal secondary metabolites

The development of targeted therapies for breast cancer, particularly those focusing on cyclin-dependent kinases 4/6 (CDK4/6), has significantly improved patient outcomes. However, the currently approved CDK4/6 inhibitors are associated with various side effects, underscoring the need for novel compounds with enhanced efficacy and safety profiles. This study aimed to identify potential CDK4/6 inhibitors from MeFSAT, a database of fungal secondary metabolites using an in-silico screening approach. The virtual screening process incorporated drug-likeness filters, ADME and toxicity predictions, consensus molecular docking, and 200 ns molecular dynamics simulations. Out of 411 initial compounds, two molecules demonstrated favorable binding interactions and stability with the CDK4/6 protein complex. The MTT assay showed that MSID000025 had dose-dependent cytotoxicity against MCF7 breast cancer cells. This suggests that MSID000025 could be a good candidate CDK4/6 inhibitor for treating breast cancer. Our study highlights the potential of fungal secondary metabolites as a source of novel compounds for drug discovery. It provides a framework for identifying CDK4/6 inhibitors with improved therapeutic properties.

Targeting STAT3, FOXO3a, and Pim-1 kinase by FDA-approved tyrosine kinase inhibitor-Radotinib: An in silico and in vitro approach

Cancer, a multifactorial pathological condition, is primarily caused due to mutations in multiple genes. Hepatocellular carcinoma (HCC) is a form of primary liver cancer that is often diagnosed at the advanced stage. Current treatment strategies for advanced HCC involve systemic therapies which are often hindered due to the emergence of resistance and toxicity. Therefore, a multitarget approach might prove more effective in HCC treatment. The present study focuses on targeting signal transducer and activator of transcription 3 (STAT3), forkhead box class O3a (FOXO3a), and proviral integration site for Moloney murine leukemia virus-1 (Pim-1) kinase, using a Food and Drug Administration (FDA)-approved anticancer drug library. Two compounds, namely, radotinib and capmatinib, were identified as top compounds using molecular docking. Among the two compounds, radotinib exhibited significant binding values towards the targeted proteins and their heterodimers. Furthermore, in vitro experiments involving 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT), live/dead, 4',6-diamidino-2-phenylindole, and clonogenic assays were performed to evaluate the effect of radotinib in human hepatoblastoma cell line/hepatocellular carcinoma cells. The gene expression data indicated reduced expression of FOXO3a and Pim-1, but no basal-level alteration of STAT3. The Western blot analysis assay showed that the phosphorylation level of STAT3 was significantly decreased upon radotinib treatment. Taken together, our findings suggest that radotinib, which is currently used in the treatment of chronic myeloid leukemia (CML), could be considered as a potential candidate for repurposing in the treatment of HCC.

Few-shot meta-learning applied to whole brain activity maps improves systems neuropharmacology and drug discovery

In this study, we present an approach to neuropharmacological research by integrating few-shot meta-learning algorithms with brain activity mapping (BAMing) to enhance the discovery of central nervous system (CNS) therapeutics. By utilizing patterns from previously validated CNS drugs, our approach facilitates the rapid identification and prediction of potential drug candidates from limited datasets, thereby accelerating the drug discovery process. The application of few-shot meta-learning algorithms allows us to adeptly navigate the challenges of limited sample sizes prevalent in neuropharmacology. The study reveals that our meta-learning-based convolutional neural network (Meta-CNN) models demonstrate enhanced stability and improved prediction accuracy over traditional machine-learning methods. Moreover, our BAM library proves instrumental in classifying CNS drugs and aiding in pharmaceutical repurposing and repositioning. Overall, this research not only demonstrates the effectiveness in overcoming data limitations but also highlights the significant potential of combining BAM with advanced meta-learning techniques in CNS drug discovery.

Semisynthetic phytochemicals in cancer treatment: a medicinal chemistry perspective

Cancer is the uncontrolled proliferation of abnormal cells that invade other areas, spread to other organs, and cause metastases, which is the most common cause of death. A review of all FDA-approved new molecular entities (NMEs) shows that natural products and derivatives account for over one-third of all NMEs. Before 1940, unmodified products and derivatives accounted for 43% and 14% of NME registrations, respectively. Since then, the share of unmodified products has decreased to 9.5% of all approved NMEs, while the share of derivatives has increased to 28%. Since the 1940s, semi-synthetic and synthetic derivatives of natural substances have gained importance, and this trend continues to date. In this study, we have discussed in detail isolated phytoconstituents with chemical modifications that are either FDA-approved or under clinical trials, such as podophyllotoxin, Taxol (paclitaxel, docetaxel), vinca alkaloids (vincristine, vinblastine), camptothecin, genistein, cephalotaxine, rohitukine, and many more, which may act as essential leads to the development of novel anticancer agents. Furthermore, we have also discussed recent developments in the most potent semisynthetic phytoconstituents, their unique properties, and their importance in cancer treatment.

Drug-Drug Interactions and Synergy: From Pharmacological Models to Clinical Application

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. SIGNIFICANCE STATEMENT: Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug-drug interactions research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Mothra: Multiobjective de novo Molecular Generation Using Monte Carlo Tree Search

In the field of drug discovery, identifying compounds that satisfy multiple criteria, such as target protein affinity, pharmacokinetics, and membrane permeability, is challenging because of the vast chemical space. Until now, multiobjective optimization via generative models has often involved linear combinations of different reward functions. Linear combinations solve multiobjective optimization problems by turning multiobjective optimization into a single-objective task and causing problems with weighting for each objective. Herein, we propose a scalable multiobjective molecular generative model developed using deep learning techniques. This model integrates the capabilities of recurrent neural networks for molecular generation and Pareto multiobjective Monte Carlo tree search to determine the optimal search direction. Through this integration, our model can generate compounds using enhanced evaluation functions that include important aspects like target protein affinity, drug similarity, and toxicity. The proposed model addresses the limitations of previous linear combination methods, and its effectiveness is demonstrated via extensive experimentation. The improvements achieved in the evaluation metrics underscore the potential utility of our approach toward drug discovery applications. In addition, we provide the source code for our model such that researchers can easily access and use our framework in their own investigations. The source code and pretrained model for Mothra, developed in this study, along with the Docker image for the Pareto front explorer and compound picker, designed to streamline the selection and visualization of optimal chemical compounds, are released under the GNU General Public License v3.0 and available at https://github.com/sekijima-lab/Mothra.

Predicting clinical trial success for Clostridium difficile infections based on preclinical data

Preclinical models are ubiquitous and essential for drug discovery, yet our understanding of how well they translate to clinical outcomes is limited. In this study, we investigate the translational success of treatments for Clostridium difficile infection from animal models to human patients. Our analysis shows that only 36% of the preclinical and clinical experiment pairs result in translation success. Univariate analysis shows that the sustained response endpoint is correlated with translation failure (SRC = -0.20, p-value = 1.53 × 10-54), and explainability analysis of multi-variate random forest models shows that both sustained response endpoint and subject age are negative predictors of translation success. We have developed a recommendation system to help plan the right preclinical study given factors such as drug dosage, bacterial dosage, and preclinical/clinical endpoint. With an accuracy of 0.76 (F1 score of 0.71) and by using only 7 features (out of 68 total), the proposed system boosts translational efficiency by 25%. The method presented can extend to any disease and can serve as a preclinical to clinical translation decision support system to accelerate drug discovery and de-risk clinical outcomes.

The landscape of drug sensitivity and resistance in sarcoma

Sarcomas are rare malignancies with over 100 distinct histological subtypes. Their rarity and heterogeneity pose significant challenges to identifying effective therapies, and approved regimens show varied responses. Novel, personalized approaches to therapy are needed to improve patient outcomes. Patient-derived tumor organoids (PDTOs) model tumor behavior across an array of malignancies. We leverage PDTOs to characterize the landscape of drug resistance and sensitivity in sarcoma, collecting 194 specimens from 126 patients spanning 24 distinct sarcoma subtypes. Our high-throughput organoid screening pipeline tested single agents and combinations, with results available within a week from surgery. Drug sensitivity correlated with clinical features such as tumor subtype, treatment history, and disease trajectory. PDTO screening can facilitate optimal drug selection and mirror patient outcomes in sarcoma. We could identify at least one FDA-approved or NCCN-recommended effective regimen for 59% of the specimens, demonstrating the potential of our pipeline to provide actionable treatment information.

Exploring Deep Eutectic Solvents as Pharmaceutical Excipients: Enhancing the Solubility of Ibuprofen and Mefenamic Acid

Objectives: The study explores the potential of various deep eutectic solvents (DESs) to serve as drug delivery systems and pharmaceutical excipients. The research focuses on two primary objectives: evaluating the ability of the selected DES systems to enhance the solubility of two poorly water-soluble model drugs (IBU and MFA), and evaluating their physicochemical properties, including density, viscosity, flow behavior, surface tension, thermal stability, and water dilution effects, to determine their suitability for pharmaceutical applications. Methods: A range of DES systems containing pharmaceutically acceptable constituents was explored, encompassing organic acid-based, sugar- and sugar alcohol-based, and hydrophobic systems, as well as menthol (MNT)-based DES systems with common pharmaceutical excipients. MNT-based DESs exhibited the most significant solubility enhancements. Results: IBU solubility reached 379.69 mg/g in MNT: PEG 400 (1:1) and 356.3 mg/g in MNT:oleic acid (1:1), while MFA solubility peaked at 17.07 mg/g in MNT:Miglyol 812®N (1:1). In contrast, solubility in hydrophilic DES systems was significantly lower, with choline chloride: glycerol (1:2) and arginine: glycolic acid (1:8) showing the best results. While demonstrating lower solubility compared to the MNT-based systems, sugar-based DESs exhibited increased tunability via water and glycerol addition both in terms of solubility and physicochemical properties, such as viscosity and surface tension. Conclusions: Our study introduces novel DES systems, expanding the repertoire of pharmaceutically acceptable DES formulations and opening new avenues for the rational design of tailored solvent systems to overcome solubility challenges and enhance drug delivery.

How can quantum computing be applied in clinical trial design and optimization?

Clinical trials are necessary for assessing the safety and efficacy of treatments. However, trial timelines are severely delayed with minimal success due to a multitude of factors, including imperfect trial site selection, cohort recruitment challenges, lack of efficacy, absence of reliable biomarkers, etc. Each of these factors possesses a unique computational challenge, such as data management, trial simulations, statistical analyses, and trial optimization. Recent advancements in quantum computing offer a promising opportunity to overcome these hurdles. In this opinion we uniquely explore the application of quantum optimization and quantum machine learning (QML) to the design and execution of clinical trials. We examine the current capabilities and limitations of quantum computing and outline its potential to streamline clinical trials.

TarKG: a comprehensive biomedical knowledge graph for target discovery

MOTIVATION

Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery.

RESULTS

We established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1 143 313 entities and 32 806 467 relations across 15 entity categories and 171 relation types, all centered around 3 core entity types: Disease, Gene, and Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences, or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG's ability to predict potential protein targets for Alzheimer's disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG.

AVAILABILITY AND IMPLEMENTATION

TarKG is accessible at https://tarkg.ddtmlab.org.

Modeling on in vivo disposition and cellular transportation of RNA lipid nanoparticles via quantum mechanics/physiologically-based pharmacokinetic approaches

The lipid nanoparticle (LNP) has been so far proven as a strongly effective delivery system for mRNA and siRNA. However, the mechanisms of LNP's distribution, metabolism, and elimination are complicated, while the transportation and pharmacokinetics (PK) of LNP are just sparsely investigated and simply described. This study aimed to build a model for the transportation of RNA-LNP in Hela cells, rats, mice, and humans by physiologically based pharmacokinetic (PBPK) and quantum mechanics (QM) models with integrated multi-source data. LNPs with different ionizable lipids, particle sizes, and doses were modeled and compared by recognizing their critical parameters dominating PK. Some interesting results were found by the models. For example, the metabolism of ionizable lipids was first limited by the LNP disassembly rate instead of the hydrolyzation of ionizable lipids; the ability of RNA release from endosomes for three ionizable lipids was quantitively derived and can predict the probability of RNA release. Moreover, the biodegradability of three ionizable lipids was estimated by the QM method and the is generally consistent with the result of PBPK result. In summary, the transportation model of RNA LNP among various species for the first time was successfully constructed. Various in vitro and in vivo pieces of evidence were integrated through QM/PBPK multi-level modeling. The resulting new understandings are related to biodegradability, safety, and RNA release ability which are highly concerned issues of the formulation. This would benefit the design and research of RNA-LNP in the future.

Nontraditional models as research tools: the road not taken

Historical reasons resulted in the almost exclusive use of a few species, most prominently Mus musculus, as the mainstream models in biomedical research. This selection was not based on Mus's distinctive relevance to human disease but rather to the pre-existing availability of resources and tools for the species that were used as models, which has enabled their adoption for research in health sciences. Unless the utilization and range of nontraditional research models expand considerably, progress in biomedical research will remain restricted within the trajectory that has been set by the existing models and their ability to provide clinically relevant information.

Genome-wide pan-GPCR cell libraries accelerate drug discovery

G protein-coupled receptors (GPCRs) are pivotal in mediating diverse physiological and pathological processes, rendering them promising targets for drug discovery. GPCRs account for about 40% of FDA-approved drugs, representing the most successful drug targets. However, only approximately 15% of the 800 human GPCRs are targeted by market drugs, leaving numerous opportunities for drug discovery among the remaining receptors. Cell expression systems play crucial roles in the GPCR drug discovery field, including novel target identification, structural and functional characterization, potential ligand screening, signal pathway elucidation, and drug safety evaluation. Here, we discuss the principles, applications, and limitations of widely used cell expression systems in GPCR-targeted drug discovery, GPCR function investigation, signal pathway characterization, and pharmacological property studies. We also propose three strategies for constructing genome-wide pan-GPCR cell libraries, which will provide a powerful platform for GPCR ligand screening, and facilitate the study of GPCR mechanisms and drug safety evaluation, ultimately accelerating the process of GPCR-targeted drug discovery.

A critical systematic review of extracellular vesicle clinical trials

This systematic review examines the landscape of extracellular vesicle (EV)-related clinical trials to elucidate the field's trends in clinical applications and EV-related methodologies, with an additional focus on the acknowledgement of EV subpopulations. By analysing data from public reporting repositories, we catalogued 471 EV-related clinical trials to date, with indications for over 200 diseases. Diagnostics and companion diagnostics represented the bulk of EV-related clinical trials with cancer being the most frequent application. EV-related therapeutics trials mainly utilized mesenchymal stromal cell (MSC) EVs and were most frequently used for treatment of respiratory illnesses. Ultracentrifugation and RNA-sequencing were the most common isolation and characterization techniques; however, methodology for each was not frequently reported in study records. Most of the reported characterization relied on bulk characterization of EV isolates, with only 11% utilizing EV subpopulations in their experimental design. While this may be connected to a lack of available techniques suitable for clinical implementation, it also highlights the opportunity for use of EV subpopulations to improve translational efforts. As academic research identifies more chemically distinct subpopulations and technologies for their enrichment, we forecast to more refined EV trials in the near future. This review emphasizes the need for meticulous methodological reporting and consideration of EV subpopulations to enhance the translational success of EV-based interventions, pointing towards a paradigm shift in personalized medicine.

A phenotypic drug discovery approach by latent interaction in deep learning

Contemporary drug discovery paradigms rely heavily on binding assays about the bio-physicochemical processes. However, this dominant approach suffers from overlooked higher-order interactions arising from the intricacies of molecular mechanisms, such as those involving cis-regulatory elements. It introduces potential impairments and restrains the potential development of computational methods. To address this limitation, I developed a deep learning model that leverages an end-to-end approach, relying exclusively on therapeutic information about drugs. By transforming textual representations of drug and virus genetic information into high-dimensional latent representations, this method evades the challenges arising from insufficient information about binding specificities. Its strengths lie in its ability to implicitly consider complexities such as epistasis and chemical-genetic interactions, and to handle the pervasive challenge of data scarcity. Through various modeling skills and data augmentation techniques, the proposed model demonstrates outstanding performance in out-of-sample validations, even in scenarios with unknown complex interactions. Furthermore, the study highlights the importance of chemical diversity for model training. While the method showcases the feasibility of deep learning in data-scarce scenarios, it reveals a promising alternative for drug discovery in situations where knowledge of underlying mechanisms is limited.

Lubricant-Coated Organ-on-a-Chip for Enhanced Precision in Preclinical Drug Testing

In drug discovery, human organ-on-a-chip (organ chip) technology has emerged as an essential tool for preclinical testing, offering a realistic representation of human physiology, real-time monitoring, and disease modeling. Polydimethylsiloxane (PDMS) is commonly used in organ chip fabrication owing to its biocompatibility, flexibility, transparency, and ability to replicate features down to the nanoscale. However, the porous nature of PDMS leads to unintended absorption of small molecules, critically affecting the drug response analysis. Addressing this challenge, the precision drug testing organ chip (PreD chip) is introduced, an innovative platform engineered to minimize small molecule absorption while facilitating cell culture. This chip features a PDMS microchannel wall coated with a perfluoropolyether-based lubricant, providing slipperiness and antifouling properties. It also incorporates an ECM-coated semi-porous membrane that supports robust multicellular cultures. The PreD chip demonstrates its outstanding antifouling properties and resistance to various biological fluids, small molecule drugs, and plasma proteins. In simulating the human gut barrier, the PreD chip demonstrates highly enhanced sensitivity in tests for dexamethasone toxicity and is highly effective in assessing drug transport across the human blood-brain barrier. These findings emphasize the potential of the PreD chip in advancing organ chip-based drug testing methodologies.

Stroke of Consistency: Streamlining Multicenter Protocols for Enhanced Reproducibility of Infarct Volumes in Preclinical Stroke Research

BACKGROUND

The discrepancy between experimental research and clinical trial outcomes is a persistent challenge in preclinical studies, particularly in stroke research. A possible factor contributing to this issue is the lack of standardization across experimental stroke models, leading to poor reproducibility in multicenter studies. This study addresses this gap by aiming to enhance reproducibility and the efficacy of multicenter studies through the harmonization of protocols and training of involved personnel.

METHODS

We established a set of standard operating procedures for various stroke models and the Neuroscore. These standard operating procedures were implemented across multiple research centers, followed by specialized, in-person training for all participants. We measured the variability in infarct volume both before and after the implementation of these standardized protocols and training sessions.

RESULTS

The standardization process led to a significant reduction in variability of infarct volume across different stroke models (40%-50% reduction), demonstrating the effectiveness of our harmonized protocols and training. Additionally, the implementation of the Neuroscore system across centers showed low variability and consistent results up to 28 days poststroke, underscoring its utility in chronic phase evaluations.

CONCLUSIONS

The harmonization of protocols and surgeon training significantly reduced variability in experimental outcomes across different centers. This improvement can increase the comparability of data between research groups and enhance the statistical power of multicenter studies. Our findings also establish the Neuroscore as a reliable tool for long-term assessment in stroke research, paving the way for more consistent and impactful multicenter preclinical studies.

Comparing Low-Dose Carvedilol Continuous Manufacturing by Solid and Liquid Feeding in Self-Emulsifying Delivery Systems via Hot Melt EXtrusion (SEDEX)

Background/Objectives: This study compared two pilot scale continuous manufacturing methods of solid self-emulsifying drug delivery systems (SEDDSs) via hot melt extrusion (HME). Methods: A model poorly water-soluble drug carvedilol in low dose (0.5-1.0% w/w) was processed in HME either in a conventional powder form or pre-dissolved in the liquid SEDDS. Results: HME yielded a processable final product with up to 20% w/w SEDDS. Addition of carvedilol powder resulted in a non-homogeneous drug distribution in the extrudates, whereas a homogeneous drug distribution was observed in pre-dissolved carvedilol. SEDDSs were shown to have a plasticizing effect, reducing the HME process torque up to 50%. Compatibility between excipients and carvedilol in the studied ratios after HME was confirmed via DSC and WAXS, demonstrating their amorphous form. Solid SEDDSs with Kollidon® VA64 self-emulsified in aqueous medium within 15 min with mean droplet sizes 150-200 nm and were independent of the medium temperature, whereas reconstitution of Soluplus® took over 60 min and mean droplet size increased 2-fold from 70 nm to 150 nm after temperature increased from 25 °C to 37 °C, indicating emulsion phase inversion at cloud point. Conclusions: In conclusion, using Kollidon® VA64 and pre-dissolved carvedilol in SEDDS has shown to yield a stabile HME process with a homogenous carvedilol content in the extrudate.

Integrating text mining with network models for successful target identification: in vitro validation in MASH-induced liver fibrosis

An in silico target discovery pipeline was developed by including a directional and weighted molecular disease network for metabolic dysfunction-associated steatohepatitis (MASH)-induced liver fibrosis. This approach integrates text mining, network biology, and artificial intelligence/machine learning with clinical transcriptome data for optimal translational power. At the mechanistic level, the critical components influencing disease progression were identified from the disease network using in silico knockouts. The top-ranked genes were then subjected to a target efficacy analysis, following which the top-5 candidate targets were validated in vitro. Three targets, including EP300, were confirmed for their roles in liver fibrosis. EP300 gene-silencing was found to significantly reduce collagen by 37%; compound intervention studies performed in human primary hepatic stellate cells and the hepatic stellate cell line LX-2 showed significant inhibition of collagen to the extent of 81% compared to the TGFβ-stimulated control (1 μM inobrodib in LX-2 cells). The validated in silico pipeline presents a unique approach for the identification of human-disease-mechanism-relevant drug targets. The directionality of the network ensures adherence to physiologically relevant signaling cascades, while the inclusion of clinical data boosts its translational power and ensures identification of the most relevant disease pathways. In silico knockouts thus provide crucial molecular insights for successful target identification.

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.

Improving tumor microenvironment assessment in chip systems through next-generation technology integration

The tumor microenvironment (TME) comprises a diverse array of cells, both cancerous and non-cancerous, including stromal cells and immune cells. Complex interactions among these cells play a central role in driving cancer progression, impacting critical aspects such as tumor initiation, growth, invasion, response to therapy, and the development of drug resistance. While targeting the TME has emerged as a promising therapeutic strategy, there is a critical need for innovative approaches that accurately replicate its complex cellular and non-cellular interactions; the goal being to develop targeted, personalized therapies that can effectively elicit anti-cancer responses in patients. Microfluidic systems present notable advantages over conventional in vitro 2D co-culture models and in vivo animal models, as they more accurately mimic crucial features of the TME and enable precise, controlled examination of the dynamic interactions among multiple human cell types at any time point. Combining these models with next-generation technologies, such as bioprinting, single cell sequencing and real-time biosensing, is a crucial next step in the advancement of microfluidic models. This review aims to emphasize the importance of this integrated approach to further our understanding of the TME by showcasing current microfluidic model systems that integrate next-generation technologies to dissect cellular intra-tumoral interactions across different tumor types. Carefully unraveling the complexity of the TME by leveraging next generation technologies will be pivotal for developing targeted therapies that can effectively enhance robust anti-tumoral responses in patients and address the limitations of current treatment modalities.

Knowledge Graphs for drug repurposing: a review of databases and methods

Drug repurposing has emerged as a effective and efficient strategy to identify new treatments for a variety of diseases. One of the most effective approaches for discovering potential new drug candidates involves the utilization of Knowledge Graphs (KGs). This review comprehensively explores some of the most prominent KGs, detailing their structure, data sources, and how they facilitate the repurposing of drugs. In addition to KGs, this paper delves into various artificial intelligence techniques that enhance the process of drug repurposing. These methods not only accelerate the identification of viable drug candidates but also improve the precision of predictions by leveraging complex datasets and advanced algorithms. Furthermore, the importance of explainability in drug repurposing is emphasized. Explainability methods are crucial as they provide insights into the reasoning behind AI-generated predictions, thereby increasing the trustworthiness and transparency of the repurposing process. We will discuss several techniques that can be employed to validate these predictions, ensuring that they are both reliable and understandable.

Alternative Cancer Therapeutics: Unpatentable Compounds and Their Potential in Oncology

Cancer remains a leading cause of death globally. Cancer patients often seek alternative therapies in addition to, or instead of, conventional treatments like chemotherapy, radiation, and surgery. The progress in medical advancements and early detection provides more treatment options; however, the development of cancer drugs requires a significant amount of time, demands substantial investments, and results in an overall low percent of regulatory approval. The complex relationship between patent protection and pharmaceutical innovation complicates cancer drug development and contributes to high mortality rates. Adjusting patent criteria for alternative cancer therapeutics could stimulate innovation, enhance treatment options, and ultimately improve outcomes for cancer patients. This article explores the potential of alternative cancer therapeutics, chemopreventive agents, natural products, off-patent drugs, generic unpatentable chemicals, and repurposed drugs in cancer treatment, emphasizing the mechanisms and therapeutic potential of these unconventional compounds as combinatorial cancer therapies. The biological pathways, therapeutic effects, and potential to enhance existing therapies are reviewed, demonstrating their cost-effective and accessible options as adjuvant cancer therapies.

Screening a Compound Library to Identify Additives That Boost Cytochrome P450 Enzyme Function in Vascularised Liver Spheres

To accurately study human organ function and disease 'in the dish', it is necessary to develop reliable cell-based models that closely track human physiology. Our interest lay with the liver, which is the largest solid organ in the body. The liver is a multifunctional and highly regenerative organ; however, severe liver damage can have dire consequences for human health. A common cause of liver damage is adverse reactions to prescription drugs. Therefore, the development of predictive liver models that capture human drug metabolism patterns is required to optimise the drug development process. In our study, we aimed to identify compounds that could improve the metabolic function of stem cell-derived liver tissue. Therefore, we screened a compound library to identify additives that improved the maturity of in vitro-engineered human tissue, with the rationale that by taking such an approach, we would be able to fine-tune neonatal and adult cytochrome P450 metabolic function in stem cell-derived liver tissue.

Synthetic Approaches, Properties, and Applications of Acylals in Preparative and Medicinal Chemistry

Diesters of geminal diols (R-CH(O-CO-R')2, RR'C(OCOR″)2, etc. with R = H, aryl or alkyl) are termed acylals according to IUPAC recommendations (Rule P-65.6.3.6 Acylals) if the acids involved are carboxylic acids. Similar condensation products can be obtained from various other acidic structures as well, but these related "non-classical acylals", as one might call them, differ in various aspects from classical acylals and will not be discussed in this article. Carboxylic acid diesters of geminal diols play a prominent role in organic chemistry, not only in their application as protective groups for aldehydes and ketones but also as precursors in the total synthesis of natural compounds and in a variety of organic reactions. What is more, acylals are useful as a key structural motif in clinically validated prodrug approaches. In this review, we summarise the syntheses and chemical properties of such classical acylals and show what potentially under-explored possibilities exist in the field of drug design, especially prodrugs, and classify this functional group in medicinal chemistry.

Protein Structure Inspired Discovery of a Novel Inducer of Anoikis in Human Melanoma

Drug discovery historically starts with an established function, either that of compounds or proteins. This can hamper discovery of novel therapeutics. As structure determines function, we hypothesized that unique 3D protein structures constitute primary data that can inform novel discovery. Using a computationally intensive physics-based analytical platform operating at supercomputing speeds, we probed a high-resolution protein X-ray crystallographic library developed by us. For each of the eight identified novel 3D structures, we analyzed binding of sixty million compounds. Top-ranking compounds were acquired and screened for efficacy against breast, prostate, colon, or lung cancer, and for toxicity on normal human bone marrow stem cells, both using eight-day colony formation assays. Effective and non-toxic compounds segregated to two pockets. One compound, Dxr2-017, exhibited selective anti-melanoma activity in the NCI-60 cell line screen. In eight-day assays, Dxr2-017 had an IC50 of 12 nM against melanoma cells, while concentrations over 2100-fold higher had minimal stem cell toxicity. Dxr2-017 induced anoikis, a unique form of programmed cell death in need of targeted therapeutics. Our findings demonstrate proof-of-concept that protein structures represent high-value primary data to support the discovery of novel acting therapeutics. This approach is widely applicable.

Combined Chemo- and Photothermal Therapies of Non-Small Cell Lung Cancer Using Polydopamine/Au Hollow Nanospheres Loaded with Doxorubicin

Purpose

The chemotherapeutic agent doxorubicin (DOX) is limited by its cardiotoxicity, posing challenges in its application for non-small cell lung cancer (NSCLC). This study aims to explore the efficacy of polydopamine/Au nanoparticles loaded with DOX for chemotherapy and photothermal therapy in NSCLC to achieve enhanced efficacy and reduced toxicity.

Methods

Hollow polydopamine (HPDA)/Au@DOX was synthesized via polydopamine chemical binding sacrificial template method. Morphology was characterized using transmission electron microscopy, particle size and potential were determined using dynamic light scattering, and photothermal conversion efficiency was assessed using near-infrared (NIR) thermal imaging. Drug loading rate and in vitro drug release were investigated. In vitro, anti-tumor experiments were conducted using CCK-8 assay, flow cytometry, and live/dead cell staining to evaluate the cytotoxicity of HPDA/Au@DOX on A549 cells. Uptake of HPDA/Au@DOX by A549 cells was detected using the intrinsic fluorescence of DOX. The in vivo anti-metastasis and anti-tumor effects of HPDA/Au@DOX were explored in mouse lung metastasis and subcutaneous tumor models, respectively.

Results

HPDA/Au@DOX with a particle size of (164.26±3.25) nm, a drug loading rate of 36.31%, and an encapsulation efficiency of 90.78% was successfully prepared. Under 808 nm laser irradiation, HPDA/Au@DOX accelerated DOX release and enhanced uptake by A549 cells. In vitro photothermal performance assessment showed excellent photothermal conversion capability and stability of HPDA/Au@DOX under NIR laser irradiation. Both in vitro and in vivo experiments demonstrated that the photothermal-chemotherapy combination group (HPDA/Au@DOX+NIR) exhibited stronger anti-metastatic and anti-tumor activities compared to the monotherapy group (DOX).

Conclusion

HPDA/Au@DOX nanosystem demonstrated excellent photothermal effect, inhibiting the growth and metastasis of A549 cells. This nanosystem achieves the combined effect of chemotherapy and photothermal, making it promising for NSCLC treatment.

Dual-Purpose Aptamer-Based Sensors for Real-Time, Multiplexable Monitoring of Metabolites in Cell Culture Media

The availability of high-frequency, real-time measurements of the concentrations of specific metabolites in cell culture systems will enable a deeper understanding of cellular metabolism and facilitate the application of good laboratory practice standards in cell culture protocols. However, currently available approaches to this end either are constrained to single-time-point and single-parameter measurements or are limited in the range of detectable analytes. Electrochemical aptamer-based (EAB) biosensors have demonstrated utility in real-time monitoring of analytes in vivo in blood and tissues. Here, we characterize a pH-sensing capability of EAB sensors that is independent of the specific target analyte of the aptamer sequence. We applied this dual-purpose EAB to the continuous measurement of pH and phenylalanine in several in vitro cell culture settings. The miniature EAB sensor that we developed exhibits rapid response times, good stability, high repeatability, and biologically relevant sensitivity. We also developed and characterized a leak-free reference electrode that mitigates the potential cytotoxic effects of silver ions released from conventional reference electrodes. Using the resulting dual-purpose sensor, we performed hourly measurements of pH and phenylalanine concentrations in the medium superfusing cultured epithelial tumor cell lines (A549, MDA-MB-23) and a human fibroblast cell line (MRC-5) for periods of up to 72 h. Our scalable technology may be multiplexed for high-throughput monitoring of pH and multiple analytes in support of the broad metabolic qualification of microphysiological systems.