A comprehensive review of key factors affecting the efficacy of antibody drug conjugate

Update of antibody-drug conjugates for hematological malignancies

PURPOSE OF REVIEW

Antibody-drug conjugates (ADCs), consisting of monoclonal antibodies (mAbs) covalently linked to cytotoxic drugs via chemical linkers, are a kind of promising tumor immunotherapy. ADCs also face a number of challenges, including unavoidable adverse effects, drug resistance, tumor targeting and payload release. To address these issues, in addition to optimizing the individual components of ADCs, such as new payloads, linkage sites and new targets, and using bispecific antibodies to increase precision, attention should be paid to optimizing the dosage of ADCs.

RECENT FINDINGS

There are currently 7 ADCs approved for marketing by the Food and Drug Administration (FDA) for hematological malignancies, and dozens of other ADCs are either in clinical trials or in the process of applying for marketing. In recent clinical studies targeting ADCs in hematologic malignancies, in addition to validating effectiveness in different indications, researchers have attempted to combine ADCs with other chemotherapeutic agents in anticipation of increased therapeutic efficacy. Furthermore, the availability of bispecific antibodies may increase the safety and efficacy of ADCs.

SUMMARY

This review summarized the progress of research on ADCs in hematological malignancies, the challenges being faced, and possible future directions to improve the efficacy of ADCs, which can provide novel insight into the future exploration of ADCs in the treatment of hematological malignancies.

A New Age of Drug Delivery: A Comparative Perspective of Ferritin-Drug Conjugates (FDCs) and Antibody-Drug Conjugates (ADCs)

Ferritin-drug conjugates (FDCs) and antibody-drug conjugates (ADCs) respectively represent the innovative and traditional mainstream approaches in drug delivery systems, each offering unique advantages and challenges. This viewpoint delves into the evolving landscape of drug delivery technologies, specifically focusing on FDCs and ADCs. Each method exhibits unique advantages and inherent challenges, shaping their roles in therapeutic applications. The article provides a comparative analysis of two delivery systems, FDCs and ADCs, in terms of targeting accuracy, drug loading capacity, and the nature of the payload itself. This comparison offers valuable insights into the distinct advantages and disadvantages associated with each system, enabling a clearer understanding of their potential applications and limitations in therapeutic contexts. This analysis is crucial for optimizing the use of these delivery systems across varying medical contexts, offering a comprehensive overview of their impact on the field of drug delivery.

The clinical landscape of antibody-drug conjugates in endometrial cancer

Clinical outcomes remain challenging in advanced or recurrent endometrial cancer due to tumor heterogeneity and therapy resistance. Antibody-drug conjugates are a novel class of cancer therapeutics, representing a promising treatment option for endometrial cancer. Antibody-drug conjugates consist of a high-affinity antibody linked to a cytotoxic payload through a stable linker. After binding to specific antigens on tumor cells, the drug is internalized, and the payload is released. In addition, the free intracellular drug may be released outside the target cell through a 'bystander effect' and kill neighboring cells, which is crucial in treating malignancies characterized by heterogeneous biomarker expression like endometrial cancer.This article aims to provide a comprehensive overview of the current clinical landscape of antibody-drug conjugates in the treatment of endometrial cancer. We conducted a thorough analysis of recent clinical trials focusing on efficacy, safety profiles, and the mechanisms by which antibody-drug conjugates target endometrial cancer. We focused particularly on the most promising antibody-drug conjugate targets in endometrial cancer under clinical investigation, such as human epidermal growth factor receptor 2 (HER2), folate receptor alpha (FRα), trophoblast cell-surface antigen-2 (TROP2), and B7-H4. We also briefly comment on the challenges, including the emergence of resistance mechanisms, and future development directions (especially agents targeting multiple antigens, combinatorial strategies, and sequential use of agents targeting the same antigen but using different payloads) in antibody-drug conjugate therapy for endometrial cancer.

Single-Domain Antibodies as Antibody-Drug Conjugates: From Promise to Practice-A Systematic Review

BACKGROUND

Antibody-drug conjugates (ADCs) represent potent cancer therapies that deliver highly toxic drugs to tumor cells precisely, thus allowing for targeted treatment and significantly reducing off-target effects. Despite their effectiveness, ADCs can face limitations due to acquired resistance and potential side effects.

OBJECTIVES

This study focuses on advances in various ADC components to improve both the efficacy and safety of these agents, and includes the analysis of several novel ADC formats. This work assesses whether the unique features of VHHs-such as their small size, enhanced tissue penetration, stability, and cost-effectiveness-make them a viable alternative to conventional antibodies for ADCs and reviews their current status in ADC development.

METHODS

Following PRISMA guidelines, this study focused on VHHs as components of ADCs, examining advancements and prospects from 1 January 2014 to 30 June 2024. Searches were conducted in PubMed, Cochrane Library, ScienceDirect and LILACS using specific terms related to ADCs and single-domain antibodies. Retrieved articles were rigorously evaluated, excluding duplicates and non-qualifying studies. The selected peer-reviewed articles were analyzed for quality and synthesized to highlight advancements, methods, payloads, and future directions in ADC research.

RESULTS

VHHs offer significant advantages for drug conjugation over conventional antibodies due to their smaller size and structure, which enhance tissue penetration and enable access to previously inaccessible epitopes. Their superior stability, solubility, and manufacturability facilitate cost-effective production and expand the range of targetable antigens. Additionally, some VHHs can naturally cross the blood-brain barrier or be easily modified to favor their penetration, making them promising for targeting brain tumors and metastases. Although no VHH-drug conjugates (nADC or nanoADC) are currently in the clinical arena, preclinical studies have explored various conjugation methods and linkers.

CONCLUSIONS

While ADCs are transforming cancer treatment, their unique mechanisms and associated toxicities challenge traditional views on bioavailability and vary with different tumor types. Severe toxicities, often linked to compound instability, off-target effects, and nonspecific blood cell interactions, highlight the need for better understanding. Conversely, the rapid distribution, tumor penetration, and clearance of VHHs could be advantageous, potentially reducing toxicity by minimizing prolonged exposure. These attributes make single-domain antibodies strong candidates for the next generation of ADCs, potentially enhancing both efficacy and safety.

Better safe than sorry: dual targeting antibodies for cancer immunotherapy

Antibody-based therapies are revolutionizing cancer treatment and experience a steady increase from preclinical and clinical pipelines to market share. While the clinical success of monoclonal antibodies is frequently limited by low response rates, treatment resistance and various other factors, multispecific antibodies open up new prospects by addressing tumor complexity as well as immune response actuation potently improving safety and efficacy. Novel antibody approaches involve simultaneous binding of two antigens on one cell implying increased specificity and reduced tumor escape for dual tumor-associated antigen targeting and enhanced and durable cytotoxic effects for dual immune cell-related antigen targeting. This article reviews antibody and cell-based therapeutics for oncology with intrinsic dual targeting of either tumor cells or immune cells. As revealed in various preclinical studies and clinical trials, dual targeting molecules are promising candidates constituting the next generation of antibody drugs for fighting cancer.

Recent Advances in Targeted Cancer Therapy: Are PDCs the Next Generation of ADCs?

Antibody-drug conjugates (ADCs) comprise antibodies, cytotoxic payloads, and linkers, which can integrate the advantages of antibodies and small molecule drugs to achieve targeted cancer treatment. However, ADCs also have some shortcomings, such as non-negligible drug resistance, a low therapeutic index, and payload-related toxicity. Many studies have focused on changing the composition of ADCs, and some have even further extended the concept and types of targeted conjugated drugs by replacing the targeted antibodies in ADCs with peptides, revolutionarily introducing peptide-drug conjugates (PDCs). This Perspective summarizes the current research status of ADCs and PDCs and highlights the structural innovations of ADC components. In particular, PDCs are regarded as the next generation of potential targeted drugs after ADCs, and the current challenges of PDCs are analyzed. Our aim is to offer fresh insights for the efficient design and expedited development of innovative targeted conjugated drugs.

Integrated edge information and pathway topology for drug-disease associations

Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.

Exploring treatment options in cancer: Tumor treatment strategies

Traditional therapeutic approaches such as chemotherapy and radiation therapy have burdened cancer patients with onerous physical and psychological challenges. Encouragingly, the landscape of tumor treatment has undergone a comprehensive and remarkable transformation. Emerging as fervently pursued modalities are small molecule targeted agents, antibody-drug conjugates (ADCs), cell-based therapies, and gene therapy. These cutting-edge treatment modalities not only afford personalized and precise tumor targeting, but also provide patients with enhanced therapeutic comfort and the potential to impede disease progression. Nonetheless, it is acknowledged that these therapeutic strategies still harbour untapped potential for further advancement. Gaining a comprehensive understanding of the merits and limitations of these treatment modalities holds the promise of offering novel perspectives for clinical practice and foundational research endeavours. In this review, we discussed the different treatment modalities, including small molecule targeted drugs, peptide drugs, antibody drugs, cell therapy, and gene therapy. It will provide a detailed explanation of each method, addressing their status of development, clinical challenges, and potential solutions. The aim is to assist clinicians and researchers in gaining a deeper understanding of these diverse treatment options, enabling them to carry out effective treatment and advance their research more efficiently.

Integrated ML-Based Strategy Identifies Drug Repurposing for Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) affects an estimated global population of around 3 million individuals. IPF is a medical condition with an unknown cause characterized by the formation of scar tissue in the lungs, leading to progressive respiratory disease. Currently, there are only two FDA-approved small molecule drugs specifically for the treatment of IPF and this has created a demand for the rapid development of drugs for IPF treatment. Moreover, denovo drug development is time and cost-intensive with less than a 10% success rate. Drug repurposing currently is the most feasible option for rapidly making the drugs to market for a rare and sporadic disease. Normally, the repurposing of drugs begins with a screening of FDA-approved drugs using computational tools, which results in a low hit rate. Here, an integrated machine learning-based drug repurposing strategy is developed to significantly reduce the false positive outcomes by introducing the predock machine-learning-based predictions followed by literature and GSEA-assisted validation and drug pathway prediction. The developed strategy is deployed to 1480 FDA-approved drugs and to drugs currently in a clinical trial for IPF to screen them against "TGFB1", "TGFB2", "PDGFR-a", "SMAD-2/3", "FGF-2", and more proteins resulting in 247 total and 27 potentially repurposable drugs. The literature and GSEA validation suggested that 72 of 247 (29.14%) drugs have been tried for IPF, 13 of 247 (5.2%) drugs have already been used for lung fibrosis, and 20 of 247 (8%) drugs have been tested for other fibrotic conditions such as cystic fibrosis and renal fibrosis. Pathway prediction of the remaining 142 drugs was carried out resulting in 118 distinct pathways. Furthermore, the analysis revealed that 29 of 118 pathways were directly or indirectly involved in IPF and 11 of 29 pathways were directly involved. Moreover, 15 potential drug combinations are suggested for showing a strong synergistic effect in IPF. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating IPF and other related conditions.

Repurposing lipid-lowering drugs on asthma and lung function: evidence from a genetic association analysis

OBJECTIVE

To explore the correlation between asthma risk and genetic variants affecting the expression or function of lipid-lowering drug targets.

METHODS

We conducted Mendelian randomization (MR) analyses using variants in several genes associated with lipid-lowering medication targets: HMGCR (statin target), PCSK9 (alirocumab target), NPC1L1 (ezetimibe target), APOB (mipomersen target), ANGPTL3 (evinacumab target), PPARA (fenofibrate target), and APOC3 (volanesorsen target), as well as LDLR and LPL. Our objective was to investigate the relationship between lipid-lowering drugs and asthma through MR. Finally, we assessed the efficacy and stability of the MR analysis using the MR Egger and inverse variance weighted (IVW) methods.

RESULTS

The elevated triglyceride (TG) levels associated with the APOC3, and LPL targets were found to increase asthma risk. Conversely, higher LDL-C levels driven by LDLR were found to decrease asthma risk. Additionally, LDL-C levels (driven by APOB, NPC1L1 and HMGCR targets) and TG levels (driven by the LPL target) were associated with improved lung function (FEV1/FVC). LDL-C levels driven by PCSK9 were associated with decreased lung function (FEV1/FVC).

CONCLUSION

In conclusion, our findings suggest a likely causal relationship between asthma and lipid-lowering drugs. Moreover, there is compelling evidence indicating that lipid-lowering therapies could play a crucial role in the future management of asthma.

Antibody-Drug Conjugates in Urothelial Cancer: From Scientific Rationale to Clinical Development

Antibody-drug conjugates (ADCs) have been a significant advancement in cancer therapy, particularly for urothelial cancer (UC). These innovative treatments, originally developed for hematological malignancies, use target-specific monoclonal antibodies linked to potent cytotoxic agents. This rational drug design efficiently delivers cancer cell-killing agents to cells expressing specific surface proteins, which are abundant in UC owing to their high antigen expression. UC is an ideal candidate for ADC therapy, as it enhances on-target efficacy while mitigating systemic toxicity. In recent years, considerable progress has been made in understanding the biology and mechanisms of tumor progression in UC. However, despite the introduction of immune checkpoint inhibitors, advanced UC is characterized by rapid progression and poor survival rates. Targeted therapies that have been developed include the anti-nectin 4 ADC enfortumab vedotin and the fibroblast growth factor receptor inhibitor erdafitinib. Enfortumab vedotin has shown efficacy in prospective studies in patients with advanced UC, alone and in combination with pembrolizumab. The anti-Trop-2 ADC sacituzumab govitecan has also demonstrated effectiveness in single-armed studies. This review highlights the mechanism of action of ADCs, their application in mono- and combination therapies, primary mechanisms of resistance, and future perspectives for their clinical use in UC treatment. ADCs have proven to be an increasingly vital component of the therapeutic landscape for urothelial carcinoma, filling a gap in the treatment of this progressive disease.

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention

BACKGROUND

Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level.

METHODS

In this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules.

RESULTS

To evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI.

CONCLUSIONS

AERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.

Development of small molecule-drug conjugates based on derivatives of natural proteasome inhibitors that exhibit selectivity for PSMA-expressing cancer cells

In this study, we have developedsmall molecule drug conjugates (SMDCs)consisting ofa prostate specific membrane antigen (PSMA) ligandand syringolin derivatives, which are potent proteasome inhibitors, to selectively deliver syringolin derivatives to prostate cancer cells. Two parent compounds were used for syringolin derivatives with different linkage sites. These SMDCs exhibited PSMA-expressing cell-selective cytotoxicity and they could potentially be used for safer treatment of cancer.

Efficacy and safety of antibody-drug conjugates in the treatment of urothelial cell carcinoma: a systematic review and meta-analysis of prospective clinical trials

Introduction: Urothelial carcinoma (UC) is a refractory disease for which achieving satisfactory outcomes remains challenging with current surgical interventions. Antibody-drug conjugates (ADCs) are a novel class of targeted therapeutics that have demonstrated encouraging results for UC. Although there is a limited number of high-quality randomized control trials (RCTs) examining the use of ADCs in patients with UC, some prospective non-randomized studies of interventions (NRSIs) provide valuable insights and pertinent information. We aim to assess the efficacy and safety of ADCs in patients with UC, particularly those with locally advanced and metastatic diseases. Methods: A systematic search was conducted across PubMed, Embase, the Cochrane Library, and Web of Science databases to identify pertinent studies. Outcomes, such as the overall response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), adverse events (AEs), and treatment-related adverse events (TRAEs), were extracted for further analyses. Results: Twelve studies involving 1,311 patients were included in this meta-analysis. In terms of tumor responses, the pooled ORR and DCR were 40% and 74%, respectively. Regarding survival analysis, the pooled median PFS and OS were 5.66 months and 12.63 months, respectively. The pooled 6-month PFS and OS were 47% and 80%, while the pooled 1-year PFS and OS were 22% and 55%, respectively. The most common TRAEs of the ADCs were alopecia (all grades: 45%, grades ≥ III: 0%), decreased appetite (all grades: 34%, grades ≥ III: 3%), dysgeusia (all grades: 40%, grades ≥ III: 0%), fatigue (all grades: 39%, grades ≥ III: 5%), nausea (all grades: 45%, grades ≥ III: 2%), peripheral sensory neuropathy (all grades: 37%, grades ≥ III: 2%), and pruritus (all grades: 32%, grades ≥ III: 1%). Conclusion: The meta-analysis in this study demonstrates that ADCs have promising efficacies and safety for patients with advanced or metastatic UC. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42023460232.

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments

BACKGROUND

Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients.

METHODS

Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients.

RESULTS

The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker.

CONCLUSION

The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

Half-life extension of single-domain antibody-drug conjugates by albumin binding moiety enhances antitumor efficacy

Single-domain antibody-drug conjugates (sdADCs) have been proven to have deeper solid tumor penetration and intratumor accumulation capabilities due to their smaller size compared with traditional IgG format ADCs. However, one of the key challenges for improving clinical outcomes of sdADCs is their abbreviated in vivo half-life. In this study, we innovatively fused an antihuman serum albumin (αHSA) nanobody to a sdADCs targeting oncofetal antigen 5T4, conferring serum albumin binding to enhance the pharmacokinetic profiles of sdADCs. The fusion protein was conjugated with monomethyl auristatin E (MMAE) at s224c site mutation. The conjugate exhibited potent cytotoxicity against various tumor cells. Compared with the nonalbumin-binding counterparts, the conjugate exhibited a 10-fold extended half-life in wild-type mice and fivefold prolonged serum half-life in BxPC-3 xenograft tumor models as well as enhanced tumor accumulation and retention in mice. Consequently, n501-αHSA-MMAE showed potent antitumor effects, which were comparable to n501-MMAE in pancreatic cancer BxPC-3 xenograft tumor models; however, in human ovarian teratoma PA-1 xenograft tumor models, n501-αHSA-MMAE significantly improved antitumor efficacy. Moreover, the conjugate showed mitigated hepatotoxicity. In summary, our results suggested that fusion to albumin-binding moiety as a viable strategy can enhance the therapeutic potential of sdADCs through optimized pharmacokinetics.

Tribulations and future opportunities for artificial intelligence in precision medicine

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

Rational Identification of Novel Antibody-Drug Conjugate with High Bystander Killing Effect against Heterogeneous Tumors

Bystander-killing payloads can significantly overcome the tumor heterogeneity issue and enhance the clinical potential of antibody-drug conjugates (ADC), but the rational design and identification of effective bystander warheads constrain the broader implementation of this strategy. Here, graph attention networks (GAT) are constructed for a rational bystander killing scoring model and ADC construction workflow for the first time. To generate efficient bystander-killing payloads, this model is utilized for score-directed exatecan derivatives design. Among them, Ed9, the most potent payload with satisfactory permeability and bioactivity, is further used to construct ADC. Through linker optimization and conjugation, novel ADCs are constructed that perform excellent anti-tumor efficacy and bystander-killing effect in vivo and in vitro. The optimal conjugate T-VEd9 exhibited therapeutic efficacy superior to DS-8201 against heterogeneous tumors. These results demonstrate that the effective scoring approach can pave the way for the discovery of novel ADC with promising bystander payloads to combat tumor heterogeneity.

Pioneering the Way: The Revolutionary Potential of Antibody-Drug Conjugates in NSCLC

OPINION STATEMENT

Despite targeted therapy and immunotherapy being recognized as established frontline treatments for advanced non-small cell lung cancer (NSCLC), the unavoidable development of resistance and disease progression poses ongoing challenges. Antibody-drug conjugates (ADCs) offer a potent treatment option for NSCLC through the specific delivery of cytotoxic agents to tumor cells that display distinct antigens. This review delves into the latest evidence regarding promising ADC agents for NSCLC, focusing on their targets, effectiveness, and safety assessments. Additionally, our study provides insights into managing toxicities, identifying biomarkers, devising methods to counter resistance mechanisms, tackling prevailing challenges, and outlining prospects for the clinical implementation of these innovative ADCs and combination regimens in NSCLC.

Synthesis of site-specific Fab-drug conjugates using ADP-ribosyl cyclases

Targeted delivery of small-molecule drugs via covalent attachments to monoclonal antibodies has proved successful in clinic. For this purpose, full-length antibodies are mainly used as drug-carrying vehicles. Despite their flexible conjugation sites and versatile biological activities, intact immunoglobulins with conjugated drugs, which feature relatively large molecular weights, tend to have restricted tissue distribution and penetration and low fractions of payloads. Linking small-molecule therapeutics to other formats of antibody may lead to conjugates with optimal properties. Here, we designed and synthesized ADP-ribosyl cyclase-enabled fragment antigen-binding (Fab) drug conjugates (ARC-FDCs) by utilizing CD38 catalytic activity. Through rapidly forming a stable covalent bond with a nicotinamide adenine dinucleotide (NAD+ )-based drug linker at its active site, CD38 genetically fused with Fab mediates robust site-specific drug conjugations via enzymatic reactions. Generated ARC-FDCs with defined drug-to-Fab ratios display potent and antigen-dependent cytotoxicity against breast cancer cells. This work demonstrates a new strategy for developing site-specific FDCs. It may be applicable to different antibody scaffolds for therapeutic conjugations, leading to novel targeted agents.

Exploring the next generation of antibody-drug conjugates

Antibody-drug conjugates (ADCs) are a promising cancer treatment modality that enables the selective delivery of highly cytotoxic payloads to tumours. However, realizing the full potential of this platform necessitates innovative molecular designs to tackle several clinical challenges such as drug resistance, tumour heterogeneity and treatment-related adverse effects. Several emerging ADC formats exist, including bispecific ADCs, conditionally active ADCs (also known as probody-drug conjugates), immune-stimulating ADCs, protein-degrader ADCs and dual-drug ADCs, and each offers unique capabilities for tackling these various challenges. For example, probody-drug conjugates can enhance tumour specificity, whereas bispecific ADCs and dual-drug ADCs can address resistance and heterogeneity with enhanced activity. The incorporation of immune-stimulating and protein-degrader ADCs, which have distinct mechanisms of action, into existing treatment strategies could enable multimodal cancer treatment. Despite the promising outlook, the importance of patient stratification and biomarker identification cannot be overstated for these emerging ADCs, as these factors are crucial to identify patients who are most likely to derive benefit. As we continue to deepen our understanding of tumour biology and refine ADC design, we will edge closer to developing truly effective and safe ADCs for patients with treatment-refractory cancers. In this Review, we highlight advances in each ADC component (the monoclonal antibody, payload, linker and conjugation chemistry) and provide more-detailed discussions on selected examples of emerging novel ADCs of each format, enabled by engineering of one or more of these components.

Peptide-Drug Conjugates: Design, Chemistry, and Drug Delivery System as a Novel Cancer Theranostic

The emergence of peptide-drug conjugates (PDCs) that utilize target-oriented peptide moieties as carriers of cytotoxic payloads, interconnected with various cleavable/noncleavable linkers, resulted in the key-foundation of the new era of targeted therapeutics. They are capable of retaining the integrity of conjugates in the blood circulatory system as well as releasing the drugs at the tumor microenvironment. Other valuable advantages are specificity and selectivity toward targeted-receptors, higher penetration ability, and drug-loading capacity, making them a suitable candidate to play their vital role as promising carrier agents. In this review, we summarized the types of cell-targeting (CTPs) and cell-penetrating peptides (CPPs) that have broad applications in the advancement of targeted drug-delivery systems (DDS). Moreover, the techniques to overcome the limitations of peptide-chemistry for their extensive implementation to construct the PDCs. Besides this, the diversified breakthrough of linker chemistry, and ample knowledge of various cytotoxic payloads used in PDCs in recent years, as well as the mechanism of action of PDCs was critically discussed. The principal aim is to provide scattered and diversified knowledge in one place and to help researchers understand the pinching knots in the science of PDC development, also their progression toward a bright future for PDCs as novel theranostics in clinical practice.

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

Recent Technological and Intellectual Property Trends in Antibody-Drug Conjugate Research

Antibody-drug conjugate (ADC) therapy, an advanced therapeutic technology comprising antibodies, chemical linkers, and cytotoxic payloads, addresses the limitations of traditional chemotherapy. This study explores key elements of ADC therapy, focusing on antibody development, linker design, and cytotoxic payload delivery. The global rise in cancer incidence has driven increased investment in anticancer agents, resulting in significant growth in the ADC therapy market. Over the past two decades, notable progress has been made, with approvals for 14 ADC treatments targeting various cancers by 2022. Diverse ADC therapies for hematologic malignancies and solid tumors have emerged, with numerous candidates currently undergoing clinical trials. Recent years have seen a noteworthy increase in ADC therapy clinical trials, marked by the initiation of numerous new therapies in 2022. Research and development, coupled with patent applications, have intensified, notably from major companies like Pfizer Inc. (New York, NY, USA), AbbVie Pharmaceuticals Inc. (USA), Regeneron Pharmaceuticals Inc. (Tarrytown, NY, USA), and Seagen Inc. (Bothell, WA, USA). While ADC therapy holds great promise in anticancer treatment, challenges persist, including premature payload release and immune-related side effects. Ongoing research and innovation are crucial for advancing ADC therapy. Future developments may include novel conjugation methods, stable linker designs, efficient payload delivery technologies, and integration with nanotechnology, driving the evolution of ADC therapy in anticancer treatment.

Network-based drug repurposing identifies small molecule drugs as immune checkpoint inhibitors for endometrial cancer

Endometrial cancer (EC) is the 6th most common cancer in women around the world. Alone in the United States (US), 66,200 new cases and 13,030 deaths are expected to occur in 2023 which needs the rapid development of potential therapies against EC. Here, a network-based drug-repurposing strategy is developed which led to the identification of 16 FDA-approved drugs potentially repurposable for EC as potential immune checkpoint inhibitors (ICIs). A network of EC-associated immune checkpoint proteins (ICPs)-induced protein interactions (P-ICP) was constructed. As a result of network analysis of P-ICP, top key target genes closely interacting with ICPs were shortlisted followed by network proximity analysis in drug-target interaction (DTI) network and pathway cross-examination which identified 115 distinct pathways of approved drugs as potential immune checkpoint inhibitors. The presented approach predicted 16 drugs to target EC-associated ICPs-induced pathways, three of which have already been used for EC and six of them possess immunomodulatory properties providing evidence of the validity of the strategy. Classification of the predicted pathways indicated that 15 drugs can be divided into two distinct pathway groups, containing 17 immune pathways and 98 metabolic pathways. In addition, drug-drug correlation analysis provided insight into finding useful drug combinations. This fair and verified analysis creates new opportunities for the quick repurposing of FDA-approved medications in clinical trials.

ADCdb: the database of antibody-drug conjugates

Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

Antibody-drug conjugates in cancer therapy: innovations, challenges, and future directions

The emergence of antibody-drug conjugates (ADCs) as a potential therapeutic avenue in cancer treatment has garnered significant attention. By combining the selective specificity of monoclonal antibodies with the cytotoxicity of drug molecules, ADCs aim to increase the therapeutic index, selectively targeting cancer cells while minimizing systemic toxicity. Various ADCs have been licensed for clinical usage, with ongoing research paving the way for additional options. However, the manufacture of ADCs faces several challenges. These include identifying suitable target antigens, enhancing antibodies, linkers, and payloads, and managing resistance mechanisms and side effects. This review focuses on the strategies to overcome these hurdles, such as site-specific conjugation techniques, novel antibody formats, and combination therapy. Our focus lies on current advancements in antibody engineering, linker technology, and cytotoxic payloads while addressing the challenges associated with ADC development. Furthermore, we explore the future potential of personalized medicine, leveraging individual patients' molecular profiles, to propel ADC treatments forward. As our understanding of the molecular mechanisms driving cancer progression continues to expand, we anticipate the development of new ADCs that offer more effective and personalized therapeutic options for cancer patients.

The Fundamental Role of Oxime and Oxime Ether Moieties in Improving the Physicochemical and Anticancer Properties of Structurally Diverse Scaffolds

The present review explores the critical role of oxime and oxime ether moieties in enhancing the physicochemical and anticancer properties of structurally diverse molecular frameworks. Specific examples are carefully selected to illustrate the distinct contributions of these functional groups to general strategies for molecular design, modulation of biological activities, computational modeling, and structure-activity relationship studies. An extensive literature search was conducted across three databases, including PubMed, Google Scholar, and Scifinder, enabling us to create one of the most comprehensive overviews of how oximes and oxime ethers impact antitumor activities within a wide range of structural frameworks. This search focused on various combinations of keywords or their synonyms, related to the anticancer activity of oximes and oxime ethers, structure-activity relationships, mechanism of action, as well as molecular dynamics and docking studies. Each article was evaluated based on its scientific merit and the depth of the study, resulting in 268 cited references and more than 336 illustrative chemical structures carefully selected to support this analysis. As many previous reviews focus on one subclass of this extensive family of compounds, this report represents one of the rare and fully comprehensive assessments of the anticancer potential of this group of molecules across diverse molecular scaffolds.

Network-based drug repurposing for HPV-associated cervical cancer

In women, cervical cancer (CC) is the fourth most common cancer around the world with average cases of 604,000 and 342,000 deaths per year. Approximately 50% of high-grade CC are attributed to human papillomavirus (HPV) types 16 and 18. Chances of CC in HPV-positive patients are 6 times more than HPV-negative patients which demands timely and effective treatment. Repurposing of drugs is considered a viable approach to drug discovery which makes use of existing drugs, thus potentially reducing the time and costs associated with de-novo drug discovery. In this study, we present an integrative drug repurposing framework based on a systems biology-enabled network medicine platform. First, we built an HPV-induced CC protein interaction network named HPV2C following the CC signatures defined by the omics dataset, obtained from GEO database. Second, the drug target interaction (DTI) data obtained from DrugBank, and related databases was used to model the DTI network followed by drug target network proximity analysis of HPV-host associated key targets and DTIs in the human protein interactome. This analysis identified 142 potential anti-HPV repurposable drugs to target HPV induced CC pathways. Third, as per the literature survey 51 of the predicted drugs are already used for CC and 33 of the remaining drugs have anti-viral activity. Gene set enrichment analysis of potential drugs in drug-gene signatures and in HPV-induced CC-specific transcriptomic data in human cell lines additionally validated the predictions. Finally, 13 drug combinations were found using a network based on overlapping exposure. To summarize, the study provides effective network-based technique to quickly identify suitable repurposable drugs and drug combinations that target HPV-associated CC.

Conditional activation of an anti-IgM antibody-drug conjugate for precise B cell lymphoma targeting

Cancerous B cells are almost indistinguishable from their non-malignant counterparts regarding their surface antigen expression. Accordingly, the challenge to be faced consists in elimination of the malignant B cell population while maintaining a functional adaptive immune system. Here, we present an IgM-specific antibody-drug conjugate masked by fusion of the epitope-bearing IgM constant domain. Antibody masking impaired interaction with soluble pentameric as well as cell surface-expressed IgM molecules rendering the antibody cytotoxically inactive. Binding capacity of the anti-IgM antibody drug conjugate was restored upon conditional protease-mediated demasking which consequently enabled target-dependent antibody internalization and subsequent induction of apoptosis in malignant B cells. This easily adaptable approach potentially provides a novel mechanism of clonal B cell lymphoma eradication to the arsenal available for non-Hodgkin's lymphoma treatment.

A comprehensive overview on antibody-drug conjugates: from the conceptualization to cancer therapy

Antibody-Drug Conjugates (ADCs) represent an innovative class of potent anti-cancer compounds that are widely used in the treatment of hematologic malignancies and solid tumors. Unlike conventional chemotherapeutic drug-based therapies, that are mainly associated with modest specificity and therapeutic benefit, the three key components that form an ADC (a monoclonal antibody bound to a cytotoxic drug via a chemical linker moiety) achieve remarkable improvement in terms of targeted killing of cancer cells and, while sparing healthy tissues, a reduction in systemic side effects caused by off-tumor toxicity. Based on their beneficial mechanism of action, 15 ADCs have been approved to date by the market approval by the Food and Drug Administration (FDA), the European Medicines Agency (EMA) and/or other international governmental agencies for use in clinical oncology, and hundreds are undergoing evaluation in the preclinical and clinical phases. Here, our aim is to provide a comprehensive overview of the key features revolving around ADC therapeutic strategy including their structural and targeting properties, mechanism of action, the role of the tumor microenvironment and review the approved ADCs in clinical oncology, providing discussion regarding their toxicity profile, clinical manifestations and use in novel combination therapies. Finally, we briefly review ADCs in other pathological contexts and provide key information regarding ADC manufacturing and analytical characterization.

Antibody-Drug Conjugates for Breast Cancer Treatment: Emerging Agents, Targets and Future Directions

To achieve the scheme of "magic bullets" in antitumor therapy, antibody-drug conjugates (ADCs) were developed. ADCs consist of antibodies targeting tumor-specific antigens, chemical linkers, and cytotoxic payloads that powerfully kill cancer cells. With the approval of ado-trastuzumab emtansine (T-DM1) and fam-trastuzumab deruxtecan (T-DXd), the therapeutic potentials of ADCs in breast cancer have come into the spotlight. Nearly 30 ADCs for breast cancer are under exploration to move targeted therapy forward. In this review, we summarize the presenting and emerging agents and targets of ADCs. The ADC structure and development history are also concluded. Moreover, the challenges faced and prospected future directions in this field are reviewed, which give insights into novel treatments with ADCs for breast cancer.

Novel formats of antibody conjugates: recent advances in payload diversity, conjugation, and linker chemistry

INTRODUCTION

In the field of bioconjugates, the focus on antibody - drug conjugates (ADCs) with novel payloads beyond the traditional categories of potent cytotoxic agents is increasing. These innovative ADCs exhibit various molecular formats, ranging from small-molecule payloads, such as immune agonists and proteolytic agents, to macromolecular payloads, such as oligonucleotides and proteins.

AREAS COVERED

This review offers an in-depth exploration of unconventional strategies for designing conjugates with novel mechanisms of action and notable examples of approaches that show promising prospects. Representative examples of novel format payloads and their classification, attributes, and appropriate conjugation techniques are discussed in detail.

EXPERT OPINION

The existing basic technologies used to manufacture ADCs can be directly applied to synthesize novel formatted conjugates. However, a wide variety of new payloads require the creation of customized technologies adapted to the unique characteristics of these payloads. Consequently, fundamental technologies, such as conjugation methods aimed at achieving high drug - antibody ratios and developing stable crosslinkers, are likely to become increasingly important research areas in the future.

Sulfur-containing marine natural products as leads for drug discovery and development

Among the large series of marine natural products (MNPs), sulfur-containing MNPs have emerged as potential therapeutic agents for the treatment of a range of diseases. Herein, we reviewed 95 new sulfur-containing MNPs isolated during the period between 2021 and March 2023. In addition, we discuss that the widely used strategies and the emerging technologies including natural product-based antibody drug conjugates (ADCs), small-molecule-based proteolysis targeting chimeras (PROTACs), nanotechnology-based drug carriers, artificial intelligence (AI)-driven drug discovery have been used for improving the efficiency and success rate of NP-based drug development. We also provide perspectives regarding the challenges and opportunities in sulfur-containing MNPs based drug discovery and development and future research directions.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

Revolutionizing drug development: harnessing the potential of organ-on-chip technology for disease modeling and drug discovery

The inefficiency of existing animal models to precisely predict human pharmacological effects is the root reason for drug development failure. Microphysiological system/organ-on-a-chip technology (organ-on-a-chip platform) is a microfluidic device cultured with human living cells under specific organ shear stress which can faithfully replicate human organ-body level pathophysiology. This emerging organ-on-chip platform can be a remarkable alternative for animal models with a broad range of purposes in drug testing and precision medicine. Here, we review the parameters employed in using organ on chip platform as a plot mimic diseases, genetic disorders, drug toxicity effects in different organs, biomarker identification, and drug discoveries. Additionally, we address the current challenges of the organ-on-chip platform that should be overcome to be accepted by drug regulatory agencies and pharmaceutical industries. Moreover, we highlight the future direction of the organ-on-chip platform parameters for enhancing and accelerating drug discoveries and personalized medicine.