Challenges In Drug Development Research Articles

Enhancing the Predictive Power of Machine Learning Models through a Chemical Space Complementary DEL Screening Strategy.

DNA-encoded library (DEL) technology is an effective method for small molecule drug discovery, enabling high-throughput screening against target proteins. While DEL screening produces extensive data, it can reveal complex patterns not easily recognized by human analysis. Lead compounds from DEL screens often have higher molecular weights, posing challenges for drug development. This study refines traditional DELs by integrating alternative techniques like photocross-linking screening to enhance chemical diversity. Combining these methods improved predictive performance for small molecule identification models. Using this approach, we predicted active small molecules for BRD4 and p300, achieving hit rates of 26.7 and 35.7%. Notably, the identified compounds exhibit smaller molecular weights and better modification potential compared to traditional DEL molecules. This research demonstrates the synergy between DEL and AI technologies, enhancing drug discovery.

Deep Learning-Assisted Compound Bioactivity Estimation Framework

Drug Discovery is a highly complicated process. On average, it takes six to twelve years to manufacture a new drug and have the product released in the market. It is of utmost importance to find methods that would accelerate the manufacturing process. This significant challenge in drug development can be addressed using deep learning techniques. The aim of this paper is to propose a deep learning-based framework that can help chemists examine compound biological activity in a more accurate manner. The proposed framework employs autoencoder for data representation of the compounds data, which is then classified using deep neural network followed by building a customized deep regression model to estimate an accurate value of the compound bioactivity. The proposed framework achieved an accuracy of 89% in autoencoder reconstruction error, 79.01% in classification, and MAE of 2.4 while predicting compound bioactivity using deep regression model.

Therapeutic targeting of senescent cells in the CNS.

Senescent cells accumulate throughout the body with advanced age, diseases and chronic conditions. They negatively impact health and function of multiple systems, including the central nervous system (CNS). Therapies that target senescent cells, broadly referred to as senotherapeutics, recently emerged as potentially important treatment strategies for the CNS. Promising therapeutic approaches involve clearing senescent cells by disarming their pro-survival pathways with 'senolytics'; or dampening their toxic senescence-associated secretory phenotype (SASP) using 'senomorphics'. Following the pioneering discovery of first-generation senolytics dasatinib and quercetin, dozens of additional therapies have been identified, and several promising targets are under investigation. Although potentially transformative, senotherapies are still in early stages and require thorough testing to ensure reliable target engagement, specificity, safety and efficacy. The limited brain penetrance and potential toxic side effects of CNS-acting senotherapeutics pose challenges for drug development and translation to the clinic. This Review assesses the potential impact of senotherapeutics for neurological conditions by summarizing preclinical evidence, innovative methods for target and biomarker identification, academic and industry drug development pipelines and progress in clinical trials.

Quantitative Systems Pharmacology Models: Potential Tools for Advancing Drug Development for Rare Diseases.

Rare diseases, affecting millions globally, present significant drug development challenges. This is due to the limited patient populations and the unique pathophysiology of these diseases, which can make traditional clinical trial designs unfeasible. Quantitative Systems Pharmacology (QSP) models offer a promising approach to expedite drug development, particularly in rare diseases. QSP models provide a mechanistic representation of the disease and drug response in virtual patients that can complement routinely applied empirical modeling and simulation approaches. QSP models can generate digital twins of actual patients and mechanistically simulate the disease progression of rare diseases, accounting for phenotypic heterogeneity. QSP models can also support drug development in various drug modalities, such as gene therapy. Impactful QSP models case studies are presented here to illustrate their value in supporting various aspects of drug development in rare indications. As these QSP model applications continue to mature, there is a growing possibility that they could be more widely integrated into routine drug development steps. This integration could provide a robust framework for addressing some of the inherent challenges in rare disease drug development.

Development of DuoMYC: a synthetic cell penetrant miniprotein that efficiently inhibits the oncogenic transcription factor MYC.

The master regulator transcription factor MYC is implicated in numerous human cancers, and its targeting is a long-standing challenge in drug development. MYC is a typical 'undruggable' target, with no binding pockets on its DNA binding domain and extensive intrinsically disordered regions. Rather than trying to target MYC directly with classical modalities, here we engineer synthetic miniproteins that can bind to MYC's target DNA, the enhancer box (E-Box), and potently inhibit MYC-driven transcription. We crafted the miniproteins via structure-based design and a combination of solid phase peptide synthesis and site-specific crosslinking. Our lead variant, DuoMYC, binds to E-Box DNA with high affinity (KD ~ 0.1 µM) and is able to enter cells and inhibit MYC-driven transcription with submicromolar potency (IC50 = 464 nM) as shown by reporter gene assay and confirmed by RNA sequencing. Notably, DuoMYC surpasses the efficacy of several other recently developed MYC inhibitors. Our results highlight the potential of engineered synthetic protein therapeutics for addressing challenging intracellular targets.

Development of DuoMYC: a synthetic cell penetrant miniprotein that efficiently inhibits the oncogenic transcription factor MYC

The master regulator transcription factor MYC is implicated in numerous human cancers, and its targeting is a long‐standing challenge in drug development. MYC is a typical ‘undruggable’ target, with no binding pockets on its DNA binding domain and extensive intrinsically disordered regions. Rather than trying to target MYC directly with classical modalities, here we engineer synthetic miniproteins that can bind to MYC’s target DNA, the enhancer box (E‐Box), and potently inhibit MYC‐driven transcription. We crafted the miniproteins via structure‐based design and a combination of solid phase peptide synthesis and site‐specific crosslinking. Our lead variant, DuoMYC, binds to E‐Box DNA with high affinity (KD ~ 0.1 µM) and is able to enter cells and inhibit MYC‐driven transcription with submicromolar potency (IC50 = 464 nM) as shown by reporter gene assay and confirmed by RNA sequencing. Notably, DuoMYC surpasses the efficacy of several other recently developed MYC inhibitors. Our results highlight the potential of engineered synthetic protein therapeutics for addressing challenging intracellular targets.

Reversal Gene Expression Assessment for Drug Repurposing, a Case Study of Glioblastoma.

Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement. This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database. We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes' log2 foldchange (LFCs) that the drug candidates could induce. Among eight prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells. The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.

Clinical Trial Findings and Drug Development Challenges for Curcumin in Infectious Disease Prevention and Treatment.

Since ancient times, turmeric, scientifically known as Curcuma longa, has been renowned for its therapeutic properties. Recently, extensive documentation has highlighted the prevalence of microbial diseases without effective treatments, the increased expense of certain antimicrobial medications, and the growing occurrence of antimicrobial drug resistance. Experts predict that drug resistance will emerge as a significant global cause of death by the middle of this century, thereby necessitating intervention. Curcumin, a major curcuminoid molecule, has shown extensive antimicrobial action. Improving and altering the use of natural antimicrobial agents is the most effective approach to addressing issues of targeted specificity and drug resistance in chemically synthesized medicines. Further research is required to explore the efficacy of curcumin and other natural antimicrobial substances in combating microbial infections. The solubility and bioavailability of curcumin impede its antimicrobial capability. To enhance curcumin's antimicrobial effectiveness, researchers have recently employed several methods, including the development of curcumin-based nanoformulations. This review seeks to compile the latest available literature to assess the advantages of curcumin as a natural antimicrobial agent (particularly antiviral and antibacterial) and strategies to enhance its medical efficacy. The future application of curcumin will help to alleviate microbial infections, thereby promoting the sustainability of the world's population.

Medicinal Plants as Effective Antiviral Agents and Their Potential Benefits

This paper explores a diverse field of medicinal plants as potential antiviral agents, and delves into utilization of plant medicine for combating viral infections, emphasizing recent surge in research on natural products from plants as antiviral agents. Specific plant-derived compounds, like phyllanthin and iscador, have been proven to exhibit antiviral properties with great potential for pharmaceutical development. Mechanisms of antiviral action by phytochemicals that are present in medicinal plants, including direct viral inhibition, degradation of viral capsid, and immunomodulation were identified, and a combination therapy of medicinal plants with conventional antiviral drugs were explored. Efficacy of medicinal plants as antiviral agents was critically assessed and revealed that the complexity and variability of herbal formulations, and safety concerns regarding toxicity levels, pose challenges in drug development. However, research on medicinal plants is often hindered by limited understanding of phytochemical mechanisms, the complexity and variability of herbal formulations, and safety concerns regarding toxicity and interactions with other medications. The way forward in harnessing full potential of medicinal plants as antiviral agents underscores the need for further research into developing models that seek to enhance the selectivity of plant extracts in order to minimize toxicity levels.

From Plan to Pivot: How Model-Informed Drug Development Shaped the Dose Strategy of the Zibotentan/Dapagliflozin ZENITH Trials.

Getting the dose right is a key challenge in drug development; model-informed drug development (MIDD) provides powerful tools to shape dose strategies and inform decision making. In this tutorial, the case study of the ZENITH trials showcases how a set of clinical pharmacology and MIDD approaches informed an impactful dose strategy. The endothelin A receptor antagonist zibotentan, combined with the sodium-glucose co-transporter-2 inhibitor dapagliflozin, has yielded a robust and significant albuminuria reduction in the Phase IIb trial ZENITH-CKD and is being investigated for reduction of kidney function decline in a high-risk chronic kidney disease population in the Phase III trial ZENITH High Proteinuria. Endothelin antagonist treatment has, until now, been limited by the class effect fluid retention. ZENITH-CKD investigated a wide range of zibotentan doses based on pharmacokinetics in renal impairment, competitor-data exposure-response modeling, and clinical trial simulations. Recruitment delays reduced interim analysis data availability; here, supportive dose-response modeling recovered decision-making confidence. At trial completion, the low-dose arm enabled Phase III dose selection between Phase IIb doses. Dose-response modeling of efficacy and Kaplan-Meier analyses of tolerability identified a kidney-function-based low-dose strategy of 0.25 or 0.75 mg zibotentan (with 10 mg dapagliflozin) to balance benefit/risk in ZENITH High Proteinuria. The applied clinical pharmacology and MIDD principles enabled successful Phase IIb dose finding, rationalized and built confidence in the innovative Phase III dosing strategy and identified a potential therapeutic window for zibotentan/dapagliflozin, providing the opportunity for a significant improvement in the treatment of chronic kidney disease with high proteinuria.

Pre-Post Survey Analysis on Pharmacy Students' Perceptions of Pharmacist Roles in the Pharmaceutical Industry

The pharmaceutical industry is undergoing rapid evolution, characterized by a complex regulatory landscape and the need for diverse skill sets. This study aimed to assess pharmacy students’ perceptions of the pharmaceutical industry and the impact of a dedicated seminar on their career aspirations and knowledge. A pre-post online survey was administered to 55 undergraduate pharmacy students at the National Pharmacy Seminar 2024, hosted by Jakarta Global University. Data were analyzed using descriptive statistics and the Wilcoxon signed-rank test (p ≤0.05). Results indicate a strong preference for careers in state-owned pharmaceutical companies (63.6%) and research and development departments (34%). The seminar significantly enhanced participants’ understanding of pharmacists’ roles, industry complexities, drug development challenges, and regulatory requirements. Notably, 93% of participants reported that the seminar met their expectations and provided valuable insights for future career exploration. These findings underscore the importance of educational interventions in shaping pharmacy students’ career trajectories and aligning their knowledge with the dynamic pharmaceutical industry.

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities.

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Advancing research and development of anti-infectives for children: A clinical development perspective

The HIV treatment landscape for adults has progressed dramatically in recent decades; however, paediatric populations continue to experience delayed and limited access to effective and safe antiretroviral therapy options. Despite current incentive programmes, formulation research and development and approved drug dosing for children have been limited, particularly for neonates (aged <4 wk). Regulatory approval of drug formulations and dosing in children may lag behind adult approvals by years. Formulation and trial design adjustments complicate paediatric drug development, all of which are vital to accommodate for physiological differences, organ maturation, and rapid weight gain, which are most significant in the youngest children. To facilitate more rapid anti-infective drug development for paediatric populations, regulatory agencies provide guidelines that include extrapolating efficacy and safety data from relevant populations; using pharmacokinetic (PK) bridging and modelling to reduce sample sizes and limit the number of PK studies needed before efficacy analyses; and enrolling age- or weight-based cohorts in parallel rather than sequentially for clinical trials. Ensuring access to approved drugs poses an additional challenge, as uncertainty in demand leads to manufacturing and supply complexity with potentially higher costs that can be a barrier to uptake. Here we summarise challenges in drug development for children living with HIV, which are not unique to antiretrovirals. We aim to propose strategies for how model-based approaches and global partnerships can overcome some of these barriers to accelerate paediatric drug development, with particular reference to HIV, and how lessons learnt from HIV could be extended to other anti-infectives.

Highly potent and broadly neutralizing anti-CD4 trimeric nanobodies inhibit HIV-1 infection by inducing CD4 conformational alteration

Despite advancements in antiretroviral therapy (ART) suppressing HIV-1 replication, existing antiviral drugs pose limitations, including lifelong medication, frequent administration, side effects and viral resistance, necessitating novel HIV-1 treatment approaches. CD4, pivotal for HIV-1 entry, poses challenges for drug development due to neutralization and cytotoxicity concerns. Nevertheless, Ibalizumab, the sole approved CD4-specific antibody for HIV-1 treatment, reignites interest in exploring alternative anti-HIV targets, emphasizing CD4’s potential value for effective drug development. Here, we explore anti-CD4 nanobodies, particularly Nb457 from a CD4-immunized alpaca. Nb457 displays high potency and broad-spectrum activity against HIV-1, surpassing Ibalizumab’s efficacy. Strikingly, engineered trimeric Nb457 nanobodies achieve complete inhibition against live HIV-1, outperforming Ibalizumab and parental Nb457. Structural analysis unveils Nb457-induced CD4 conformational changes impeding viral entry. Notably, Nb457 demonstrates therapeutic efficacy in humanized female mouse models. Our findings highlight anti-CD4 nanobodies as promising HIV-1 therapeutics, with potential implications for advancing clinical treatment against this global health challenge.

Strategies for successful dose optimization in oncology drug development: a practical guide

ABSTRACT Dose optimization is a critical challenge in drug development. Historically, dose determination in oncology has followed a divergent path from other non-oncology therapeutic areas due to the unique characteristics and requirements in Oncology. However, with the emergence of new drug modalities and mechanisms of drugs in oncology, such as immune therapies, radiopharmaceuticals, targeted therapies, cytostatic agents, and others, the dose-response relationship for efficacy and toxicity could be vastly varied compared to the cytotoxic chemotherapies. The doses below the MTD may demonstrate similar efficacy to the MTD with an improved tolerability profile, resembling what is commonly observed in non-oncology treatments. Hence, alternate strategies for dose optimization are required for new modalities in oncology drug development. This paper delves into the historical evolution of dose finding methods from non-oncology to oncology, highlighting examples and summarizing the underlying drivers of change. Subsequently, a practical framework and guidance are provided to illustrate how dose optimization can be incorporated into various stages of the development program. We provide the following general recommendations: 1) The objective for phase I is to identify a dose range rather than a single MTD dose for subsequent development to better characterize the safety and tolerability profile within the dose range. 2) At least two doses separable by PK are recommended for dose optimization in phase II. 3) Ideally, dose optimization should be performed before launching the confirmatory study. Nevertheless, innovative designs such as seamless II/III design can be implemented for dose selection and may accelerate the drug development program.

Substance Delivery across the Blood-Brain Barrier or the Blood-Retinal Barrier Using Organic Cation Transporter Novel Type 2 (OCTN2)

The membrane impermeability of a drug poses a significant challenge in drug research and development, preventing effective drug delivery to the target site. Specifically, the blood-brain barrier (BBB) presents a formidable obstacle to the delivery of drugs targeting the central nervous system (CNS) into the brain, whereas the blood-retinal barrier (BRB) presents a tremendous obstacle to the delivery of drugs targeting the ocular diseases into the eyes. The development of drugs for Alzheimer’s or Parkinson’s disease targeting the CNS and for diabetic retinopathy and age-related macular degeneration targeting the eyes remains an unmet medical need for patients. Transporters play a crucial physiological role in maintaining homeostasis in metabolic organs. Various types of solute carrier (SLC) transporters are expressed in the capillary endothelial cells of the BBB, facilitating the delivery of nutrients from the blood flow to the brain. Therefore, carrier-mediated transport across the BBB can be achieved using SLC transporters present in capillary endothelial cells. It is well-known that CNS drugs typically incorporate N-containing groups, indicating that cation transporters facilitate their transport into the brain. In fact, carrier-mediated transport across the BBB can be accomplished using glucose transporter type 1 (Glut1) as a glucose transporter, L-type amino acid transporter 1 (LAT1) as a large neutral amino acid transporter, and H+/cation antiporter as a cation transporter. Surprisingly, although organic cation transporter novel type 2 (OCTN2) is expressed in the capillary endothelial cells, there has been limited investigation into OCTN2-mediated substance delivery into the brain across the BBB. Furthermore, it is suggested that OCTN2 is expressed at the BRB. In this prospective review, I present the advantages and possibilities of substance delivery into the brain across the BBB or into the eyes across the BRB, mediated by OCTN2 via carrier-mediated transport or receptor-mediated transcytosis.

A hybrid quantum computing pipeline for real world drug discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

Membrane Fusion-Mediated Loading of Therapeutic siRNA into Exosome for Tissue-Specific Application.

Tissue-specific delivery of oligonucleotide therapeutics beyond the liver remains a key challenge in nucleic acid drug development. To address this issue, exploiting exosomes as a novel carrier has emerged as a promising approach for efficient nucleic acid drug delivery. However, current exosome-based delivery systems still face multiple hurdles in their clinical applications. Herein, this work presents a strategy for constructing a hybrid exosome vehicle (HEV) through a DNA zipper-mediated membrane fusion approach for tissue-specific siRNA delivery. As a proof-of-concept, this work successfully fuses a liposome encapsulating anti-NFKBIZ siRNAs with corneal epithelium cell (CEC)-derived exosomes to form a HEV construct for the treatment of dry eye disease (DED). With homing characteristics inherited from exosomes, the siRNA-bearing HEV can target its parent cells and efficiently deliver the siRNA payloads to the cornea. Subsequently, the NFKBIZ gene silencing significantly reduces pro-inflammatory cytokine secretions from the ocular surface, reshapes its inflammatory microenvironment, and ultimately achieves an excellent therapeutic outcome in a DED mouse model. As a versatile platform, this hybrid exosome with targeting capability and designed therapeutic siRNAs may hold great potential in various disease treatments.

Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development.

The conventional rules for anti-cancer drug development are no longer sufficient given the relatively limited number of patients available for therapeutic trials. It is thus a real challenge to better design trials in the context of new drug approval for anti-cancer treatment. Artificial intelligence (AI)-based in silico trials can incorporate far fewer but more informative patients and could be conducted faster and at a lower cost. AI can be integrated into in silico clinical trials to improve data analysis, modeling and simulation, personalized medicine approaches, trial design optimization, and virtual patient generation. Health authorities are encouraged to thoroughly review the rules for setting up clinical trials, incorporating AI and in silico methodology once they have been appropriately validated. This article also aims to highlight the limits and challenges related to AI and machine learning.

Targeting of nanoparticles to the cerebral vasculature after traumatic brain injury.

Traumatic brain injury has faced numerous challenges in drug development, primarily due to the difficulty of effectively delivering drugs to the brain. However, there is a potential solution in targeted drug delivery methods involving antibody-drug conjugates or nanocarriers conjugated with targeting antibodies. Following a TBI, the blood-brain barrier (BBB) becomes permeable, which can last for years and allow the leakage of harmful plasma proteins. Consequently, an appealing approach for TBI treatment involves using drug delivery systems that utilize targeting antibodies and nanocarriers to help restore BBB integrity. In our investigation of this strategy, we examined the efficacy of free antibodies and nanocarriers targeting a specific endothelial surface marker called vascular cell adhesion molecule-1 (VCAM-1), which is known to be upregulated during inflammation. In a mouse model of TBI utilizing central fluid percussion injury, free VCAM-1 antibody did not demonstrate superior targeting when comparing sham vs. TBI brain. However, the administration of VCAM-1-targeted nanocarriers (liposomes) exhibited a 10-fold higher targeting specificity in TBI brain than in sham control. Flow cytometry and confocal microscopy analysis confirmed that VCAM-1 liposomes were primarily taken up by brain endothelial cells post-TBI. Consequently, VCAM-1 liposomes represent a promising platform for the targeted delivery of therapeutics to the brain following traumatic brain injury.