Independent Drug Action in Combination Therapy: Implications for Precision Oncology

Design, Synthesis, and Activity Evaluation of Novel Benzazole Bifunctional Antifungal Inhibitors with an LDH Carrier

Fungal infections maintain a close relation with the body's immune function. In this study, three series of benzazole compounds were designed as dual-target (PD-L1/CYP51) inhibitors using the skeleton splicing approach; their molecular structures were synthesized and evaluated accordingly. Among them, the compounds 9a-2, 12a-2, and 12b-1 exhibited potent antifungal activity and dual-target inhibition ability. Especially, the compound 12a-2 simultaneously exerted excellent bifunctional effects of fungal inhibition and immune activation. Moreover, a layered double hydroxide (LDH) carrier was also successfully constructed based on an infection microenvironment to improve the bioavailability and the targeting of compound 12a-2. This significantly accelerated the recovery process of deep and shallow fungal infections. In conclusion, this study expanded the development horizon of antifungal drugs and provided a novel drug delivery route for treating fungal infections.

Organoid Cultures for the Study of Mammary Biology and Breast Cancer: The Promise and Challenges

During the last decade, biomedical research has experienced a resurgence in the use of three-dimensional culture models for studies of normal and cancer biology. This resurgence has been driven by the development of models in which primary cells are grown in tissue-mimicking media and extracellular matrices to create organoid or organotypic cultures that more faithfully replicate the complex architecture and physiology of normal tissues and tumors. In addition, patient-derived tumor organoids preserve the three-dimensional organization and characteristics of the patient tumors ex vivo, becoming excellent preclinical models to supplement studies of tumor xenografts transplanted into immunocompromised mice. In this perspective, we provide an overview of how organoids are being used to investigate normal mammary biology and as preclinical models of breast cancer and discuss improvements that would enhance their utility and relevance to the field.

Radiation therapy with phenotypic medicine: towards N-of-1 personalization

In current clinical practice, radiotherapy (RT) is prescribed as a pre-determined total dose divided over daily doses (fractions) given over several weeks. The treatment response is typically assessed months after the end of RT. However, the conventional one-dose-fits-all strategy may not achieve the desired outcome, owing to patient and tumor heterogeneity. Therefore, a treatment strategy that allows for RT dose personalization based on each individual response is preferred. Multiple strategies have been adopted to address this challenge. As an alternative to current known strategies, artificial intelligence (AI)-derived mechanism-independent small data phenotypic medicine (PM) platforms may be utilized for N-of-1 RT personalization. Unlike existing big data approaches, PM does not engage in model refining, training, and validation, and guides treatment by utilizing prospectively collected patient's own small datasets. With PM, clinicians may guide patients' RT dose recommendations using their responses in real-time and potentially avoid over-treatment in good responders and under-treatment in poor responders. In this paper, we discuss the potential of engaging PM to guide clinicians on upfront dose selections and ongoing adaptations during RT, as well as considerations and limitations for implementation. For practicing oncologists, clinical trialists, and researchers, PM can either be implemented as a standalone strategy or in complement with other existing RT personalizations. In addition, PM can either be used for monotherapeutic RT personalization, or in combination with other therapeutics (e.g. chemotherapy, targeted therapy). The potential of N-of-1 RT personalization with drugs will also be presented.

The therapeutic impact of programmed death - 1 in the treatment of colorectal cancer

Colorectal cancer (CRC) is the most common type of newly diagnosed cancer. Metastatic spread and multifactorial chemoresistance have limited the benefits of current therapies. Hence, it is imperative to identify new therapeutic agents to increase treatment efficacy. One of CRC's most promising immunotherapeutic targets is programmed death-1 (PD-1), a cell surface receptor that regulates immune responses. In this paper, we provide an overview of the therapeutic impact of PD-1 in the treatment of CRC. Cancer cells can exploit the PD-1 pathway by upregulating its programmed death-ligand 1 (PD-L1) ligand to evade immune surveillance. The binding of PD-L1 to PD-1 inhibits T cell function, leading to tumor immune escape. PD-1 inhibitors, such as pembrolizumab and nivolumab, block the PD-1/PD-L1 interaction. Clinical trials evaluating PD-1 inhibitors in advanced CRC have shown promising results. In patients with microsatellite instability-high (MSI-H) or mismatch repair-deficient (dMMR) tumors characterized by high mutation rates and increased immunogenicity, PD-1 blockade has demonstrated remarkable efficacy. As a result, pembrolizumab and nivolumab have received accelerated approval by regulatory authorities for the treatment of MSI-H/dMMR metastatic CRC. Additionally, combination approaches, such as combining PD-1 inhibitors with other immunotherapies or targeted agents, are being explored. Despite the success of PD-1 inhibitors in CRC, challenges still exist. Immune-related adverse events can occur and require close monitoring. In conclusion, PD-1 inhibitors have demonstrated significant therapeutic impact, particularly in patients with MSI-H/dMMR tumors.

Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data

INTRODUCTION

Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.

AREAS COVERED

Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.

EXPERT OPINION

Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.

Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors

Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors.

SIGNIFICANCE

A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.

Defeating MYC with drug combinations or dual-targeting drugs

Members of the MYC family of proteins are a major target for cancer drug discovery, but the development of drugs that block MYC-driven cancers has not yet been successful. Approaches to achieve success may include the development of combination therapies or dual-acting drugs that target MYC at multiple nodes. Such treatments hold the possibility of additive or synergistic activity, potentially reducing side effect profiles and the emergence of resistance. In this review, we examine the prominent MYC-related targets and highlight those that have been targeted in combination and/or dual-target approaches. Finally, we explore the challenges of combination and dual-target approaches from a drug development perspective.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial

Background

Predictive biomarkers of treatment response are lacking for metastatic clearcell renal cell carcinoma (ccRCC), a tumor type that is treated with angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is arguably the best candidate to predict anti-angiogenic (AA) response. However, the clinical adoption of transcriptomic assays faces several challenges including standardization, time delay, and high cost. Further, ccRCC tumors are highly heterogenous, and sampling multiple areas for sequencing is impractical.

Approach

Here we present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides. In order to overcome the lack of interpretability, one of the biggest limitations of typical DL models, our model produces a visual vascular network which is the basis of the model's prediction. To test its reliability, we applied this model to multiple cohorts including a clinical trial dataset.

Results

Our model accurately predicts the RNA-based Angioscore on multiple independent cohorts (spearman correlations of 0.77 and 0.73). Further, the predictions help unravel meaningful biology such as association of angiogenesis with grade, stage, and driver mutation status. Finally, we find our model is able to predict response to AA therapy, in both a real-world cohort and the IMmotion150 clinical trial. The predictive power of our model vastly exceeds that of CD31, a marker of vasculature, and nearly rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based Angioscore at a fraction of the cost.

Conclusion

By providing a robust yet interpretable prediction of the Angioscore from histopathology slides alone, our approach offers insights into angiogenesis biology and AA treatment response.

Structural variety and pharmacological potential of naphthylisoquinoline alkaloids

Naphthylisoquinoline alkaloids are a fascinating class of natural biaryl compounds. They show characteristic mono- and dimeric scaffolds, with chiral axes and stereogenic centers. Since the appearance of the last comprehensive overview on these secondary plant metabolites in this series in 1995, the number of discovered representatives has tremendously increased to more than 280 examples known today. Many novel-type compounds have meanwhile been discovered, among them naphthylisoquinoline-related follow-up products like e.g., the first seco-type (i.e., ring-opened) and ring-contracted analogues. As highlighted in this review, the knowledge on the broad structural chemodiversity of naphthylisoquinoline alkaloids has been decisively driven forward by extensive phytochemical studies on the metabolite pattern of Ancistrocladus abbreviatus from Coastal West Africa, which is a particularly "creative" plant. These investigations furnished a considerable number of more than 80-mostly new-natural products from this single species, with promising antiplasmodial activities and with pronounced cytotoxic effects against human leukemia, pancreatic, cervical, and breast cancer cells. Another unique feature of naphthylisoquinoline alkaloids is their unprecedented biosynthetic origin from polyketidic precursors and not, as usual for isoquinoline alkaloids, from aromatic amino acids-a striking example of biosynthetic convergence in nature. Furthermore, remarkable botanical results are presented on the natural producers of naphthylisoquinoline alkaloids, the paleotropical Dioncophyllaceae and Ancistrocladaceae lianas, including first investigations on the chemoecological role of these plant metabolites and their storage and accumulation in particular plant organs.

Metabolomics at the tumor microenvironment interface: Decoding cellular conversations

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

Combinatorial strategies to target RAS-driven cancers

Although RAS was formerly considered undruggable, various agents that inhibit RAS or specific RAS oncoproteins have now been developed. Indeed, the importance of directly targeting RAS has recently been illustrated by the clinical success of mutant-selective KRAS inhibitors. Nevertheless, responses to these agents are typically incomplete and restricted to a subset of patients, highlighting the need to develop more effective treatments, which will likely require a combinatorial approach. Vertical strategies that target multiple nodes within the RAS pathway to achieve deeper suppression are being investigated and have precedence in other contexts. However, alternative strategies that co-target RAS and other therapeutic vulnerabilities have been identified, which may mitigate the requirement for profound pathway suppression. Regardless, the efficacy of any given approach will likely be dictated by genetic, epigenetic and tumour-specific variables. Here we discuss various combinatorial strategies to treat KRAS-driven cancers, highlighting mechanistic concepts that may extend to tumours harbouring other RAS mutations. Although many promising combinations have been identified, clinical responses will ultimately depend on whether a therapeutic window can be achieved and our ability to prospectively select responsive patients. Therefore, we must continue to develop and understand biologically diverse strategies to maximize our likelihood of success.

Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations

Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible combinations is vast and responses can be context-specific. Systematic screens can identify clinically relevant, actionable combinations in defined patient subtypes. We present data for 109 anticancer drug combinations from AstraZeneca's oncology small molecule portfolio screened in 755 pan-cancer cell lines. Combinations were screened in a 7 × 7 concentration matrix, with more than 4 million measurements of sensitivity, producing an exceptionally data-rich resource. We implement a new approach using combination Emax (viability effect) and highest single agent (HSA) to assess combination benefit. We designed a clinical translatability workflow to identify combinations with clearly defined patient populations, rationale for tolerability based on tumor type and combination-specific "emergent" biomarkers, and exposures relevant to clinical doses. We describe three actionable combinations in defined cancer types, confirmed in vitro and in vivo, with a focus on hematologic cancers and apoptotic targets.

SIGNIFICANCE

We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of "emergent" combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695.

Nanomedicine-induced cell pyroptosis to enhance antitumor immunotherapy

Immunotherapy is a therapeutic modality designed to elicit or augment an immune response against malignancies. Despite the immune system's ability to detect and eradicate neoplastic cells, certain neoplastic cells can elude immune surveillance and elimination through diverse mechanisms. Therefore, antitumor immunotherapy has emerged as a propitious strategy. Pyroptosis, a type of programmed cell death (PCD) regulated by Gasdermin (GSDM), is associated with cytomembrane rupture due to continuous cell expansion, which results in the release of cellular contents that can trigger robust inflammatory and immune responses. The field of nanomedicine has made promising progress, enabling the application of nanotechnology to enhance the effectiveness and specificity of cancer therapy by potentiating, enabling, or augmenting pyroptosis. In this review, we comprehensively examine the paradigms underlying antitumor immunity, particularly paradigms related to nanotherapeutics combined with pyroptosis; these treatments include chemotherapy (CT), hyperthermia therapy, photodynamic therapy (PDT), chemodynamic therapy (CDT), ion-interference therapy (IIT), biomimetic therapy, and combination therapy. Furthermore, we thoroughly discuss the coordinated mechanisms that regulate these paradigms. This review is expected to enhance the understanding of the interplay between pyroptosis and antitumor immunotherapy, broaden the utilization of diverse nanomaterials in pyroptosis-based antitumor immunotherapy, and facilitate advancements in clinical tumor therapy.

Active metabolites combination therapies: towards the next paradigm for more efficient and more scientific Chinese medicine

Traditional Chinese medicine (TCM) formulae have been studied extensively in various human diseases and have proven to be effective due to their multi-component, multi-target advantage. However, its active metabolites are not clear and the specific mechanisms are not well established, which limits its scientific application. Recently, combination therapies are attracting increasing attention from the scientific community in the past few years and are considered as the next paradigm in drug discovery. Here, we tried to define a new concept of "active metabolites combination therapies (AMCT)" rules to elucidate how the bioactive metabolites from TCMs to produce their synergistic effects in this review. The AMCT rules integrate multidisciplinary technologies like molecular biology, biochemistry, pharmacology, analytical chemistry and pharmacodynamics, etc. Meanwhile, emerging technologies such as multi-omics combined analysis, network analysis, artificial intelligence conduce to better elucidate the mechanisms of these combination therapies in disease treatment, which provides new insights for the development of novel active metabolites combination drugs. AMCT rules will hopefully further guide the development of novel combination drugs that will promote the modernization and international needs of TCM.

Phase II Study of Eribulin plus Pembrolizumab in Metastatic Soft-tissue Sarcomas: Clinical Outcomes and Biological Correlates

PURPOSE

Eribulin modulates the tumor-immune microenvironment via cGAS-STING signaling in preclinical models. This non-randomized phase II trial evaluated the combination of eribulin and pembrolizumab in patients with soft-tissue sarcomas (STS).

PATIENTS AND METHODS

Patients enrolled in one of three cohorts: leiomyosarcoma (LMS), liposarcomas (LPS), or other STS that may benefit from PD-1 inhibitors, including undifferentiated pleomorphic sarcoma (UPS). Eribulin was administered at 1.4 mg/m2 i.v. (days 1 and 8) with fixed-dose pembrolizumab 200 mg i.v. (day 1) of each 21-day cycle, until progression, unacceptable toxicity, or completion of 2 years of treatment. The primary endpoint was the 12-week progression-free survival rate (PFS-12) in each cohort. Secondary endpoints included the objective response rate, median PFS, safety profile, and overall survival (OS). Pretreatment and on-treatment blood specimens were evaluated in patients who achieved durable disease control (DDC) or progression within 12 weeks [early progression (EP)]. Multiplexed immunofluorescence was performed on archival LPS samples from patients with DDC or EP.

RESULTS

Fifty-seven patients enrolled (LMS, n = 19; LPS, n = 20; UPS/Other, n = 18). The PFS-12 was 36.8% (90% confidence interval: 22.5-60.4) for LMS, 69.6% (54.5-89.0) for LPS, and 52.6% (36.8-75.3) for UPS/Other cohorts. All 3 patients in the UPS/Other cohort with angiosarcoma achieved RECIST responses. Toxicity was manageable. Higher IFNα and IL4 serum levels were associated with clinical benefit. Immune aggregates expressing PD-1 and PD-L1 were observed in a patient that completed 2 years of treatment.

CONCLUSIONS

The combination of eribulin and pembrolizumab demonstrated promising activity in LPS and angiosarcoma.

Modeling combination therapies in patient cohorts and cell cultures using correlated drug action

Characterizing the effect of combination therapies is vital for treating diseases like cancer. We introduce correlated drug action (CDA), a baseline model for the study of drug combinations in both cell cultures and patient populations, which assumes that the efficacy of drugs in a combination may be correlated. We apply temporal CDA (tCDA) to clinical trial data, and demonstrate the utility of this approach in identifying possible synergistic combinations and others that can be explained in terms of monotherapies. Using MCF7 cell line data, we assess combinations with dose CDA (dCDA), a model that generalizes other proposed models (e.g., Bliss response-additivity, the dose equivalence principle), and introduce Excess over CDA (EOCDA), a new metric for identifying possible synergistic combinations in cell culture.

SOX2 and OCT4 mediate radiation and drug resistance in pancreatic tumor organoids

Pancreatic cancer has a five-year survival rate of only 10%, mostly due to late diagnosis and limited treatment options. In patients with unresectable disease, either FOLFIRINOX, a combination of 5-fluorouracil (5-FU), oxaliplatin and irinotecan, or gemcitabine plus nab-paclitaxel combined with radiation are frontline standard regimens. However, chemo-radiation therapy has shown limited success because patients develop resistance to chemotherapy and/or radiation. In this study, we evaluated the role of pancreatic cancer stem cells (CSC) using OCT4 and SOX2, CSC markers in mouse pancreatic tumor organoids. We treated pancreatic tumor organoids with 4 or 8 Gy of radiation, 10 μM of 5-FU (5-Fluorouracil), and 100 μM 3-Bromopyruvate (3BP), a promising anti-cancer drug, as a single treatment modalities, and in combination with RT. Our results showed significant upregulation of, OCT4, and SOX2 expression in pancreatic tumor organoids treated with 4 and 8 Gy of radiation, and downregulation following 5-FU treatment. The expression of CSC markers with increasing treatment dose exhibited elevated upregulation levels to radiation and downregulation to 5-FU chemotherapy drug. Conversely, when tumor organoids were treated with a combination of 5-FU and radiation, there was a significant inhibition in SOX2 and OCT4 expression, indicating CSC self-renewal inhibition. Noticeably, we also observed that human pancreatic tumor tissues exhibited heterogeneous and aberrant OCT4 and SOX2 expression as compared to normal pancreas, indicating their potential role in pancreatic cancer growth and therapy resistance. In addition, the combination of 5-FU and radiation treatment exhibited significant inhibition of the β-catenin pathway in pancreatic tumor organoids, resulting in sensitization to treatment and organoid death. In conclusion, our study emphasizes the crucial role of CSCs in therapeutic resistance in PC treatment. We recommend using tumor organoids as a model system to explore the impact of CSCs in PC and identify new therapeutic targets.

Ex vivo discovery of synergistic drug combinations for hematologic malignancies

Combination therapies have improved outcomes for patients with acute myeloid leukemia (AML). However, these patients still have poor overall survival. Although many combination therapies are identified with high-throughput screening (HTS), these approaches are constrained to disease models that can be grown in large volumes (e.g., immortalized cell lines), which have limited translational utility. To identify more effective and personalized treatments, we need better strategies for screening and exploring potential combination therapies. Our objective was to develop an HTS platform for identifying effective combination therapies with highly translatable ex vivo disease models that use size-limited, primary samples from patients with leukemia (AML and myelodysplastic syndrome). We developed a system, ComboFlow, that comprises three main components: MiniFlow, ComboPooler, and AutoGater. MiniFlow conducts ex vivo drug screening with a miniaturized flow-cytometry assay that uses minimal amounts of patient sample to maximize throughput. ComboPooler incorporates computational methods to design efficient screens of pooled drug combinations. AutoGater is an automated gating classifier for flow cytometry that uses machine learning to rapidly analyze the large datasets generated by the assay. We used ComboFlow to efficiently screen more than 3000 drug combinations across 20 patient samples using only 6 million cells per patient sample. In this screen, ComboFlow identified the known synergistic combination of bortezomib and panobinostat. ComboFlow also identified a novel drug combination, dactinomycin and fludarabine, that synergistically killed leukemic cells in 35 % of AML samples. This combination also had limited effects in normal, hematopoietic progenitors. In conclusion, ComboFlow enables exploration of massive landscapes of drug combinations that were previously inaccessible in ex vivo models. We envision that ComboFlow can be used to discover more effective and personalized combination therapies for cancers amenable to ex vivo models.

The Siren Song of Synergy

SUMMARY

In ancient Greek mythology, sirens were creatures of stunning beauty whose mystical songs led sailors to sail their boats onto hidden rocks and into total destruction. In this issue, Mason-Osann and colleagues present data in the context of acute myelogenous leukemia to suggest that while synergy may show initial attractions in drug combinations, it may carry with it hazards previously unforeseen. See related article by Mason-Osann et al., p. 95 (1).

Unlocking the power of sugar: carbohydrate ligands as key players in nanotherapeutic-assisted targeted cancer therapy

Cancer cells need as much as 40-times more sugar than their normal cell counterparts. This sugar demand is attained by the excessive expression of inimitable transporters on the surface of cancer cells, driven by their voracious appetite for carbohydrates. Nanotechnological advances drive research utilizing ligand-directed therapeutics and diverse carbohydrate analogs. The precise delivery of these therapeutic cargos not only mitigates toxicity associated with chemotherapy but also reduces the grim toll of mortality and morbidity among patients. This in-depth review explores the potential of these ligands in advanced cancer treatment using nanoparticles. It offers a broader perspective beyond the usual ways we deliver drugs, potentially changing the way we fight cancer.

Celecoxib and cisplatin dual-loaded microspheres synergistically enhance transarterial chemoembolization effect of hepatocellular carcinoma

Transarterial chemoembolization (TACE) is a first-line treatment for intermediate to advanced-stage liver cancer, with drug-eluting microspheres commonly used as embolic agents. However, currently available drug-eluting microspheres suffer from low drug-loading capacity and limited drug options. In this work, we developed polydopamine-modified polyvinyl alcohol dual-drug-loaded microspheres encapsulating celecoxib and cisplatin (referred to as PCDMS). Physicochemical characterization revealed that the surface of the microspheres displayed increased roughness after polydopamine modification, and celecoxib and cisplatin were successfully loaded onto the microsphere surface. In vitro cell experiments demonstrated that the PCDMS significantly inhibited the proliferation and migration of highly metastatic human liver cancer cells (MHCC-97H) and human liver cancer cells (SMMC-7721). Furthermore, the dual-loaded microspheres exhibited remarkable tumor growth inhibition and reshaped the tumor microenvironment in both subcutaneous H22 liver cancer model in Balb/c mice and intrahepatic VX2 tumor model in New Zealand rabbits, demonstrating a synergistic antitumor effect where 1 + 1>2. This work provides a potential therapeutic approach for the treatment of refractory liver cancer and holds significant translational potential.

Amino acid derivative of probenecid potentiates apoptosis-inducing effects of vinblastine by increasing oxidative stress in a cancer cell-specific manner

Many chemotherapeutic drugs suffer from multidrug resistance (MDR). Efflux transporters, namely ATP-binding cassettes (ABCs), that pump the drugs out of the cancer cells comprise one major reason behind MDR. Therefore, ABC inhibitors have been under development for ages, but unfortunately, without clinical success. In the present study, an l-type amino acid transporter 1 (LAT1)-utilizing derivative of probenecid (PRB) was developed as a cancer cell-targeted efflux inhibitor for P-glycoprotein (P-gp), breast cancer resistant protein (BCRP) and/or several multidrug resistant proteins (MRPs), and its ability to increase vinblastine (VBL) cellular accumulation and apoptosis-inducing effects were explored. The novel amino acid derivative of PRB (2) increased the VBL exposure in triple-negative human breast cancer cells (MDA-MB-231) and human glioma cells (U-87MG) by 10-68 -times and 2-5-times, respectively, but not in estrogen receptor-positive human breast cancer cells (MCF-7). However, the combination therapy had greater cytotoxic effects in MCF-7 compared to MDA-MB-231 cells due to the increased oxidative stress recorded in MCF-7 cells. The metabolomic study also revealed that compound 2, together with VBL, decreased the transport of those amino acids essential for the biosynthesis of endogenous anti-oxidant glutathione (GSH). Moreover, the metabolic differences between the outcomes of the studied breast cancer cell lines were explained by the distinct expression profiles of solute carriers (SLCs) that can be concomitantly inhibited. Therefore, attacking several SLCs simultaneously to change the nutrient environment of cancer cells can serve as an adjuvant therapy to other chemotherapeutics, offering an alternative to ABC inhibitors.

In situ modeling of acquired resistance to RTK/RAS-pathway-targeted therapies

Intrinsic and acquired resistance limit the window of effectiveness for oncogene-targeted cancer therapies. Here, we describe an in situ resistance assay (ISRA) that reliably models acquired resistance to RTK/RAS-pathway-targeted therapies across cell lines. Using osimertinib resistance in EGFR-mutated lung adenocarcinoma (LUAD) as a model system, we show that acquired osimertinib resistance can be significantly delayed by inhibition of proximal RTK signaling using SHP2 inhibitors. Isolated osimertinib-resistant populations required SHP2 inhibition to resensitize cells to osimertinib and reduce MAPK signaling to block the effects of enhanced activation of multiple parallel RTKs. We additionally modeled resistance to targeted therapies including the KRASG12C inhibitors adagrasib and sotorasib, the MEK inhibitor trametinib, and the farnesyl transferase inhibitor tipifarnib. These studies highlight the tractability of in situ resistance assays to model acquired resistance to targeted therapies and provide a framework for assessing the extent to which synergistic drug combinations can target acquired drug resistance.

Preparation, Optimization, and In-Vitro Evaluation of Brusatol- and Docetaxel-Loaded Nanoparticles for the Treatment of Prostate Cancer

Challenges to docetaxel use in prostate cancer treatment include several resistance mechanisms as well as toxicity. To overcome these challenges and to improve the therapeutic efficacy in heterogeneous prostate cancer, the use of multiple agents that can destroy different subpopulations of the tumor is required. Brusatol, a multitarget inhibitor, has been shown to exhibit potent anticancer activity and play an important role in drug response and chemoresistance. Thus, the combination of brusatol and docetaxel in a nanoparticle platform for the treatment of prostate cancer is expected to produce synergistic effects. In this study, we reported the development of polymeric nanoparticles for the delivery of brusatol and docetaxel in the treatment of prostate cancer. The one-factor-at-a-time method was used to screen for formulation and process variables that impacted particle size. Subsequently, factors that had modifiable effects on particle size were evaluated using a 24 full factorial statistical experimental design followed by the optimization of drug loading. The optimization of blank nanoparticles gave a formulation with a mean size of 169.1 nm ± 4.8 nm, in agreement with the predicted size of 168.333 nm. Transmission electron microscopy showed smooth spherical nanoparticles. The drug release profile showed that the encapsulated drugs were released over 24 h. Combination index data showed a synergistic interaction between the drugs. Cell cycle analysis and the evaluation of caspase activity showed differences in PC-3 and LNCaP prostate cancer cell responses to the agents. Additionally, immunoblots showed differences in survivin expression in LNCaP cells after treatment with the different agents and formulations for 24 h and 72 h. Therefore, the nanoparticles are potentially suitable for the treatment of advanced prostate cancer.

Inhibition of iRhom1 by CD44-targeting nanocarrier for improved cancer immunochemotherapy

The multifaceted chemo-immune resistance is the principal barrier to achieving cure in cancer patients. Identifying a target that is critically involved in chemo-immune-resistance represents an attractive strategy to improve cancer treatment. iRhom1 plays a role in cancer cell proliferation and its expression is negatively correlated with immune cell infiltration. Here we show that iRhom1 decreases chemotherapy sensitivity by regulating the MAPK14-HSP27 axis. In addition, iRhom1 inhibits the cytotoxic T-cell response by reducing the stability of ERAP1 protein and the ERAP1-mediated antigen processing and presentation. To facilitate the therapeutic translation of these findings, we develop a biodegradable nanocarrier that is effective in codelivery of iRhom pre-siRNA (pre-siiRhom) and chemotherapeutic drugs. This nanocarrier is effective in tumor targeting and penetration through both enhanced permeability and retention effect and CD44-mediated transcytosis in tumor endothelial cells as well as tumor cells. Inhibition of iRhom1 further facilitates tumor targeting and uptake through inhibition of CD44 cleavage. Co-delivery of pre-siiRhom and a chemotherapy agent leads to enhanced antitumor efficacy and activated tumor immune microenvironment in multiple cancer models in female mice. Targeting iRhom1 together with chemotherapy could represent a strategy to overcome chemo-immune resistance in cancer treatment.

Prediction of Clinical Precision Chemotherapy by Patient-Derived 3D Bioprinting Models of Colorectal Cancer and Its Liver Metastases

Methods accurately predicting the responses of colorectal cancer (CRC) and colorectal cancer liver metastasis (CRLM) to personalized chemotherapy remain limited due to tumor heterogeneity. This study introduces an innovative patient-derived CRC and CRLM tumor model for preclinical investigation, utilizing 3d-bioprinting (3DP) technology. Efficient construction of homogeneous in vitro 3D models of CRC/CRLM is achieved through the application of patient-derived primary tumor cells and 3D bioprinting with bioink. Genomic and histological analyses affirm that the CRC/CRLM 3DP tumor models effectively retain parental tumor biomarkers and mutation profiles. In vitro tests evaluating chemotherapeutic drug sensitivities reveal substantial tumor heterogeneity in chemotherapy responses within the 3DP CRC/CRLM models. Furthermore, a robust correlation is evident between the drug response in the CRLM 3DP model and the clinical outcomes of neoadjuvant chemotherapy. These findings imply a significant potential for the application of patient-derived 3DP cancer models in precision chemotherapy prediction and preclinical research for CRC/CRLM.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review

BACKGROUND

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement.

OBJECTIVES

This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research.

METHODS

Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed.

RESULT

The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them.

CONCLUSION

In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.

Polymer Conjugate as the New Promising Drug Delivery System for Combination Therapy against Cancer

This review highlights the advantages of combination therapy using polymer conjugates as drug delivery systems for cancer treatment. In this review, the specific structures and materials of polymer conjugates, as well as the different types of combination chemotherapy strategies, are discussed. Specific targeting strategies, such as monoclonal antibody therapy and small molecule ligands, are also explored. Additionally, self-assembled polymer micelles and overcoming multidrug resistance are described as potential strategies for combination therapy. The assessment of combinational therapeutic efficacy and the challenges associated with polymer conjugates are also addressed. The future outlook aims to overcome these challenges and improve the effectiveness of drug delivery systems for combination therapy. The conclusion emphasizes the potential of polymer conjugates in combination therapy while acknowledging the need for further research and development in this field.

Hyper-Dependence on NHEJ Enables Synergy between DNA-PK Inhibitors and Low-Dose Doxorubicin in Leiomyosarcoma

PURPOSE

Leiomyosarcoma (LMS) is an aggressive sarcoma for which standard chemotherapies achieve response rates under 30%. There are no effective targeted therapies against LMS. Most LMS are characterized by chromosomal instability (CIN), resulting in part from TP53 and RB1 co-inactivation and DNA damage repair defects. We sought to identify therapeutic targets that could exacerbate intrinsic CIN and DNA damage in LMS, inducing lethal genotoxicity.

EXPERIMENTAL DESIGN

We performed clinical targeted sequencing in 287 LMS and genome-wide loss-of-function screens in 3 patient-derived LMS cell lines, to identify LMS-specific dependencies. We validated candidate targets by biochemical and cell-response assays in vitro and in seven mouse models.

RESULTS

Clinical targeted sequencing revealed a high burden of somatic copy-number alterations (median fraction of the genome altered =0.62) and demonstrated homologous recombination deficiency signatures in 35% of LMS. Genome-wide short hairpin RNA screens demonstrated PRKDC (DNA-PKcs) and RPA2 essentiality, consistent with compensatory nonhomologous end joining (NHEJ) hyper-dependence. DNA-PK inhibitor combinations with unconventionally low-dose doxorubicin had synergistic activity in LMS in vitro models. Combination therapy with peposertib and low-dose doxorubicin (standard or liposomal formulations) inhibited growth of 5 of 7 LMS mouse models without toxicity.

CONCLUSIONS

Combinations of DNA-PK inhibitors with unconventionally low, sensitizing, doxorubicin dosing showed synergistic effects in LMS in vitro and in vivo models, without discernable toxicity. These findings underscore the relevance of DNA damage repair alterations in LMS pathogenesis and identify dependence on NHEJ as a clinically actionable vulnerability in LMS.

Radiation-Induced Intestinal Injury: Injury Mechanism and Potential Treatment Strategies

Radiation-induced intestinal injury (RIII) is one of the most common intestinal complications caused by radiotherapy for pelvic and abdominal tumors and it seriously affects the quality of life of patients. However, the treatment of acute RIII is essentially symptomatic and nutritional support treatment and an ideal means of prevention and treatment is lacking. Researchers have conducted studies at the cellular and animal levels and found that some chemical or biological agents have good therapeutic effects on RIII and may be used as potential candidates for clinical treatment. This article reviews the injury mechanism and potential treatment strategies based on cellular and animal experiments to provide new ideas for the diagnosis and treatment of RIII in clinical settings.

Additivity predicts the efficacy of most approved combination therapies for advanced cancer

Most advanced cancers are treated with drug combinations. Rational design aims to identify synergistic combinations, but existing synergy metrics apply to preclinical, not clinical data. Here we propose a model of drug additivity for progression-free survival (PFS) to assess whether clinical efficacies of approved drug combinations are additive or synergistic. This model includes patient-to-patient variability in best single-drug response plus the weaker drug per patient. Among US Food and Drug Administration approvals of drug combinations for advanced cancers (1995-2020), 95% exhibited additive or less than additive effects on PFS times. Among positive or negative phase 3 trials published between 2014-2018, every combination that improved PFS was expected to succeed by additivity (100% sensitivity) and most failures were expected to fail (78% specificity). This study shows synergy is neither a necessary nor common property of clinically effective drug combinations. The predictable efficacy of approved combinations suggests that additivity can be a design principle for combination therapies.

Quenching thirst with poison? Paradoxical effect of anticancer drugs

Anticancer drugs have been developed with expectations to provide long-term or at least short-term survival benefits for patients with cancer. Unfortunately, drug therapy tends to provoke malignant biological and clinical behaviours of cancer cells relating not only to the evolution of resistance to specific drugs but also to the enhancement of their proliferation and metastasis abilities. Thus, drug therapy is suspected to impair long-term survival in treated patients under certain circumstances. The paradoxical therapeutic effects could be described as 'quenching thirst with poison', where temporary relief is sought regardless of the consequences. Understanding the underlying mechanisms by which tumours react on drug-induced stress to maintain viability is crucial to develop rational targeting approaches which may optimize survival in patients with cancer. In this review, we describe the paradoxical adverse effects of anticancer drugs, in particular how cancer cells complete resistance evolution, enhance proliferation, escape from immune surveillance and metastasize efficiently when encountered with drug therapy. We also describe an integrative therapeutic framework that may diminish such paradoxical effects, consisting of four main strategies: (1) targeting endogenous stress response pathways, (2) targeting new identities of cancer cells, (3) adaptive therapy- exploiting subclonal competition of cancer cells, and (4) targeting tumour microenvironment.

Cancer drug repurposing in autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with uncertain origins. Understanding of the mechanisms underlying ASD remains limited, and treatments are lacking. Genetic diversity complicates drug development. Given the complexity and severity of ASD symptoms and the rising number of diagnoses, exploring novel therapeutic strategies is essential. Here, we focus on shared molecular pathways between ASD and cancer and highlight recent progress on the repurposing of cancer drugs for ASD treatment, such as mTOR inhibitors, histone deacetylase inhibitors, and anti-inflammatory agents. We discuss how to improve trial design considering drug dose and patient age. Lastly, the discussion explores the critical aspects of side effects, commercial factors, and the efficiency of drug-screening pipelines; all of which are essential considerations in the pursuit of repurposing cancer drugs for addressing core features of ASD.

A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics

Combination chemotherapy is crucial for successfully treating cancer. However, the enormous number of possible drug combinations means discovering safe and effective combinations remains a significant challenge. To improve this process, we conduct large-scale targeted CRISPR knockout screens in drug-treated cells, creating a genetic map of druggable genes that sensitize cells to commonly used chemotherapeutics. We prioritize neuroblastoma, the most common extracranial pediatric solid tumor, where ~50% of high-risk patients do not survive. Our screen examines all druggable gene knockouts in 18 cell lines (10 neuroblastoma, 8 others) treated with 8 widely used drugs, resulting in 94,320 unique combination-cell line perturbations, which is comparable to the largest existing drug combination screens. Using dense drug-drug rescreening, we find that the top CRISPR-nominated drug combinations are more synergistic than standard-of-care combinations, suggesting existing combinations could be improved. As proof of principle, we discover that inhibition of PRKDC, a component of the non-homologous end-joining pathway, sensitizes high-risk neuroblastoma cells to the standard-of-care drug doxorubicin in vitro and in vivo using patient-derived xenograft (PDX) models. Our findings provide a valuable resource and demonstrate the feasibility of using targeted CRISPR knockout to discover combinations with common chemotherapeutics, a methodology with application across all cancers.

A review on Millepachine and its derivatives as potential multitarget anticancer agents

Chalcones have a long history of being used for many medical purposes. These are the most prestigious scaffolds in medicine. The potential of Millepachine and its derivatives to treat various malignancies has been demonstrated in this review. The anticancer effects of Millepachine and its derivatives on ovarian cancer, hepatocellular carcinoma, breast, liver, colon, cervical, prostate, stomach, and gliomas are highlighted in the current review. Several genes that are crucial in reducing the severity of the disease have been altered by these substances. They mainly work by preventing tubulin polymerizing. They also exhibit apoptosis and cell cycle arrest at the G2/M phase. Additionally, these compounds inhibit invasion and migration and have antiproliferative effects. Preclinical studies have shown that Millepachine and its derivatives offer exceptional potential for treating a number of cancers. These results need to be confirmed in clinical research in order to develop viable cancer therapies.

CRISPR-Cas System Is an Effective Tool for Identifying Drug Combinations That Provide Synergistic Therapeutic Potential in Cancers

Despite numerous efforts, the therapeutic advancement for neuroblastoma and other cancer treatments is still ongoing due to multiple challenges, such as the increasing prevalence of cancers and therapy resistance development in tumors. To overcome such obstacles, drug combinations are one of the promising applications. However, identifying and implementing effective drug combinations are critical for achieving favorable treatment outcomes. Given the enormous possibilities of combinations, a rational approach is required to predict the impact of drug combinations. Thus, CRISPR-Cas-based and other approaches, such as high-throughput pharmacological and genetic screening approaches, have been used to identify possible drug combinations. In particular, the CRISPR-Cas system (Clustered Regularly Interspaced Short Palindromic Repeats) is a powerful tool that enables us to efficiently identify possible drug combinations that can improve treatment outcomes by reducing the total search space. In this review, we discuss the rational approaches to identifying, examining, and predicting drug combinations and their impact.

Network pharmacology: a bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine

Network pharmacology can ascertain the therapeutic mechanism of drugs for treating diseases at the level of biological targets and pathways. The effective mechanism study of traditional Chinese medicine (TCM) characterized by multi-component, multi-targeted, and integrative efficacy, perfectly corresponds to the application of network pharmacology. Currently, network pharmacology has been widely utilized to clarify the mechanism of the physiological activity of TCM. In this review, we comprehensively summarize the application of network pharmacology in TCM to reveal its potential of verifying the phenotype and underlying causes of diseases, realizing the personalized and accurate application of TCM. We searched the literature using "TCM network pharmacology" and "network pharmacology" as keywords from Web of Science, PubMed, Google Scholar, as well as Chinese National Knowledge Infrastructure in the last decade. The origins, development, and application of network pharmacology are closely correlated with the study of TCM which has been applied in China for thousands of years. Network pharmacology and TCM have the same core idea and promote each other. A well-defined research strategy for network pharmacology has been utilized in several aspects of TCM research, including the elucidation of the biological basis of diseases and syndromes, the prediction of TCM targets, the screening of TCM active compounds, and the decipherment of mechanisms of TCM in treating diseases. However, several factors limit its application, such as the selection of databases and algorithms, the unstable quality of the research results, and the lack of standardization. This review aims to provide references and ideas for the research of TCM and to encourage the personalized and precise use of Chinese medicine.

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.

Radius additivity score: a novel combination index for tumour growth inhibition in fixed-dose xenograft studies

The effect of combination therapies in many cancers has often been shown to be superior to that of monotherapies. This success is commonly attributed to drug synergies. Combinations of two (or more) drugs in xenograft tumor growth inhibition (TGI) studies are typically designed at fixed doses for each compound. The available methods for assessing synergy in such study designs are based on combination indices (CI) and model-based analyses. The former methods are suitable for screening exercises but are difficult to verify in in vivo studies, while the latter incorporate drug synergy in semi-mechanistic frameworks describing disease progression and drug action but are unsuitable for screening. In the current study, we proposed the empirical radius additivity (Rad-add) score, a novel CI for synergy detection in fixed-dose xenograft TGI combination studies. The Rad-add score approximates model-based analysis performed using the semi-mechanistic constant-radius growth TGI model. The Rad-add score was compared with response additivity, defined as the addition of the two response values, and the bliss independence model in combination studies derived from the Novartis PDX dataset. The results showed that the bliss independence and response additivity models predicted synergistic interactions with high and low probabilities, respectively. The Rad-add score predicted synergistic probabilities that appeared to be between those predicted with response additivity and the Bliss model. We believe that the Rad-add score is particularly suitable for assessing synergy in the context of xenograft combination TGI studies, as it combines the advantages of CI approaches suitable for screening exercises with those of semi-mechanistic TGI models based on a mechanistic understanding of tumor growth.

Secretion of acetylated amino acids by drug-induced cancer cells: perspectives on metabolic-epigenetic alterations

The emerging understanding of the super-complex and heterogeneous nature of tumor is well supported by metabolic reprogramming, leading survival advantages. Metabolic reprogramming contributes to tumor responsiveness and resistance to various antitumor drugs. Among the numerous adaptations made by cancer cells in response to drug-induced perturbations, key metabolic alterations involving amino acids and acetylated derivatives of amino acids have received special attention. Considering these implications discussed, targeting cancer-associated metabolic pathways, particularly those involving acetylated amino acids, emerges as an important avenue in the pursuit of combinatorial anticancer strategies. As a result, the introduction of mimetic acetylated amino acids represents a promising new class of inhibitors that could be used alongside traditional chemotherapy agents.

Advancements in MDM2 inhibition: Clinical and pre-clinical investigations of combination therapeutic regimens

Cancer cells often depend on multiple pathways for their growth and survival, resulting in therapeutic resistance and the limited effectiveness of treatments. Combination therapy has emerged as a favorable approach to enhance treatment efficacy and minimize acquired resistance and harmful side effects. The murine double minute 2 (MDM2) protein regulates cellular proliferation and promotes cancer-related activities by negatively regulating the tumor suppressor protein p53. MDM2 aberrations have been reported in a variety of human cancers, making it an appealing target for cancer therapy. As a result, several small-molecule MDM2 inhibitors have been developed and are currently being investigated in clinical studies. Nevertheless, it has been shown that the inhibition of MDM2 alone is inadequate to achieve long-term suppression of tumor growth, thus prompting the need for further investigation into combination therapeutic strategies. In this review, possible clinical and preclinical MDM2 combination inhibitor regimens are thoroughly analyzed and discussed. It provides a rationale for combining MDM2 inhibitors with other therapeutic approaches in the management of cancer, taking into consideration ongoing clinical trials that evaluate the combination of MDM2 inhibitors. The review explores the current status of MDM2 inhibitors in combination with chemotherapy or targeted therapy, as well as promising approach of combining MDM2 inhibitors with immunotherapy. In addition, it investigates the function of PROTACs as MDM2 degraders in cancer treatment. A comprehensive examination of these combination regimens highlights the potential for advancing MDM2-inhibitor therapy and improving clinical outcomes for cancer patients and establishes the foundation for future research and development in this promising area of study.

Interpreting Randomized Controlled Trials

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

Development and validation of HPLC method for simultaneous estimation of erlotinib and niclosamide from liposomes optimized by screening design

The emerging drug resistance to the approved first-line drug therapy leads to clinical failure in cancer. Drug repurposing studies lead to the identification of many old drugs to be used for cancer treatment. Combining the repurposed drugs (niclosamide) with first-line therapy agents like erlotinib HCl showed improved efficacy by inhibiting erlotinib HCl acquired resistance. But there is a need to develop a sensitive, accurate, and excellent analytical method and drug delivery system for successfully delivering drug combinations. In the current study, an HPLC method was developed and validated for the simultaneous estimation of niclosamide and erlotinib HCl. The retention time of niclosamide and erlotinib hydrochloride was 6.48 and 7.65 min at 333 nm. The developed method was rapid and sensitive to separating the two drugs with reasonable accuracy, precision, robustness, and ruggedness. A Plackett-Burman (PBD) screening design was used to identify the critical parameters affecting liposomal formulation development using particle size, size distribution, zeta potential, and entrapment efficiency as the response. Lipid concentration, drug concentration, hydration temperature, and media volume were critical parameters affecting the particle size, polydispersity index (PDI), ZP, and %EE of the liposomes. The optimized NCM-ERL liposomes showed the particle size (126.05 ± 2.1), PDI (0.498 ± 0.1), ZP (-16.2 ± 0.3), and %EE of NCM and ERL (50.04 ± 2.8 and 05.42 ± 1.3). In vitro release studies indicated the controlled release of the drugs loaded liposomes (87.06 ± 9.93% and 42.33 ± 0.89% in 24 h).

Application of intelligent responsive DNA self-assembling nanomaterials in drug delivery

Smart nanomaterials are nano-scaled materials that respond in a controllable and reversible way to external physical or chemical stimuli. DNA self-assembly is an effective way to construct smart nanomaterials with precise structure, diverse functions and wide applications. Among them, static structures such as DNA polyhedron, DNA nanocages and DNA hydrogels, as well as dynamic reactions such as catalytic hairpin reaction, hybridization chain reaction and rolling circle amplification, can serve as the basis for building smart nanomaterials. Due to the advantages of DNA, such as good biocompatibility, simple synthesis, rational design, and good stability, these materials have attracted increasing attention in the fields of pharmaceuticals and biology. Based on their specific response design, DNA self-assembled smart nanomaterials can deliver a variety of drugs, including small molecules, nucleic acids, proteins and other drugs; and they play important roles in enhancing cellular uptake, resisting enzymatic degradation, controlling drug release, and so on. This review focuses on different assembly methods of DNA self-assembled smart nanomaterials, therapeutic strategies based on various intelligent responses, and their applications in drug delivery. Finally, the opportunities and challenges of smart nanomaterials based on DNA self-assembly are summarized.

Drug Discovery Targeting Nuclear Receptor Binding SET Domain Protein 2 (NSD2)

Nuclear receptor binding SET domain proteins (NSDs) catalyze the mono- or dimethylation of histone 3 lysine 36 (H3K36me1 and H3K36me2), using S-adenosyl-l-methionine (SAM) as a methyl donor. As a key member of the NSD family of proteins, NSD2 plays an important role in the pathogenesis and progression of various diseases such as cancers, inflammations, and infectious diseases, serving as a promising drug target. Developing potent and specific NSD2 inhibitors may provide potential novel therapeutics. Several NSD2 inhibitors and degraders have been discovered while remaining in the early stage of drug development. Excitingly, KTX-1001, a selective NSD2 inhibitor, has entered clinical trials. In this Perspective, the structures and functions of NSD2, its roles in various human diseases, and the recent advances in drug discovery strategies targeting NSD2 have been summarized. The challenges, opportunities, and future directions for developing NSD2 inhibitors and degraders are also discussed.

Chemotherapy-Mediated Neuronal Aberration

Chemotherapy is a life-sustaining therapeutic option for cancer patients. Despite the advancement of several modern therapies, such as immunotherapy, gene therapy, etc., chemotherapy remains the first-line therapy for most cancer patients. Along with its anti-cancerous effect, chemotherapy exhibits several detrimental consequences that restrict its efficacy and long-term utilization. Moreover, it effectively hampers the quality of life of cancer patients. Cancer patients receiving chemotherapeutic drugs suffer from neurological dysfunction, referred to as chemobrain, that includes cognitive and memory dysfunction and deficits in learning, reasoning, and concentration ability. Chemotherapy exhibits neurotoxicity by damaging the DNA in neurons by interfering with the DNA repair system and antioxidant machinery. In addition, chemotherapy also provokes inflammation by inducing the release of various pro-inflammatory cytokines, including NF-kB, IL-1β, IL-6, and TNF-α. The chemotherapy-mediated inflammation contributes to chemobrain in cancer patients. These inflammatory cytokines modulate several growth signaling pathways and reactive oxygen species homeostasis leading to systemic inflammation in the body. This review is an effort to summarize the available information which discusses the role of chemotherapy-induced inflammation in chemobrain and how it impacts different aspects of therapeutic outcome and the overall quality of life of the patient. Further, this article also discusses the potential of herbal-based remedies to overcome chemotherapy-mediated neuronal toxicity as well as to improve the quality of life of cancer patients.

The Future of Precision Oncology

Our understanding of the molecular mechanisms underlying cancer development and evolution have evolved rapidly over recent years, and the variation from one patient to another is now widely recognized. Consequently, one-size-fits-all approaches to the treatment of cancer have been superseded by precision medicines that target specific disease characteristics, promising maximum clinical efficacy, minimal safety concerns, and reduced economic burden. While precision oncology has been very successful in the treatment of some tumors with specific characteristics, a large number of patients do not yet have access to precision medicines for their disease. The success of next-generation precision oncology depends on the discovery of new actionable disease characteristics, rapid, accurate, and comprehensive diagnosis of complex phenotypes within each patient, novel clinical trial designs with improved response rates, and worldwide access to novel targeted anticancer therapies for all patients. This review outlines some of the current technological trends, and highlights some of the complex multidisciplinary efforts that are underway to ensure that many more patients with cancer will be able to benefit from precision oncology in the near future.

Integrated multiomic analysis and high-throughput screening reveal potential gene targets and synergetic drug combinations for osteosarcoma therapy

Although great advances have been made over the past decades, therapeutics for osteosarcoma are quite limited. We performed long-read RNA sequencing and tandem mass tag (TMT)-based quantitative proteome on osteosarcoma and the adjacent normal tissues, next-generation sequencing (NGS) on paired osteosarcoma samples before and after neoadjuvant chemotherapy (NACT), and high-throughput drug combination screen on osteosarcoma cell lines. Single-cell RNA sequencing data were analyzed to reveal the heterogeneity of potential therapeutic target genes. Additionally, we clarified the synergistic mechanisms of doxorubicin (DOX) and HDACs inhibitors for osteosarcoma treatment. Consequently, we identified 2535 osteosarcoma-specific genes and several alternative splicing (AS) events with osteosarcoma specificity and/or patient heterogeneity. Hundreds of potential therapeutic targets were identified among them, which showed the core regulatory roles in osteosarcoma. We also identified 215 inhibitory drugs and 236 synergistic drug combinations for osteosarcoma treatment. More interestingly, the multiomic analysis pointed out the pivotal role of HDAC1 and TOP2A in osteosarcoma. HDAC inhibitors synergized with DOX to suppress osteosarcoma both in vitro and in vivo. Mechanistically, HDAC inhibitors synergized with DOX by downregulating SP1 to transcriptionally modulate TOP2A expression. This study provided a comprehensive view of molecular features, therapeutic targets, and synergistic drug combinations for osteosarcoma.