Cost Of New Drug Development Research Articles

ParaCPI: A Parallel Graph Convolutional Network for Compound-Protein Interaction Prediction.

Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up possibilities for leveraging known biological knowledge to predict unknown CPIs. However, existing CPI prediction models still fall short of meeting the needs of practical drug discovery applications. A novel parallel graph convolutional network model for CPI prediction (ParaCPI) is proposed in this study. This model constructs feature representation of compounds using a unique approach to predict unknown CPIs from known CPI data more effectively. Experiments are conducted on five public datasets, and the results are compared with current state-of-the-art (SOTA) models under three different experimental settings to evaluate the model's performance. In the three cold-start settings, ParaCPI achieves an average performance gain of 26.75%, 23.84%, and 14.68% in terms of area under the curve compared with the other SOTA models. In addition, the results of the experiments in the case study show ParaCPI's superior ability to predict unknown CPIs based on known data, with higher accuracy and stronger generalization compared with the SOTA models. Researchers can leverage ParaCPI to accelerate the drug discovery process.

Rational construction of a novel fluorescent probe for imaging peroxynitrite in the endoplasmic reticulum during drug-induced liver injury

Drug-induced liver injury (DILI) not only poses a serious health threat to patients, but is also a major obstacle to the development of new drugs. The liver toxicity of many drugs is not detected until late in the development process or even after marketing, which significantly increases the cost of new drug development. This may be limited by the lack of tools to detect DILI. Studies have shown that abnormal changes in peroxynitrite (ONOO-) levels in the endoplasmic reticulum of liver cells may be an early manifestation of DILI. Therefore, the development of fluorescent probes to detect ONOO- in the endoplasmic reticulum may provide assistance in the detection of DILI and the screening of new drugs. Herein, we report a fluorescent probe, ER-N, capable of detecting ONOO- in the endoplasmic reticulum with high selectivity and sensitivity. The probe selects di(trifluoromethyl)benzene as the targeting group of the endoplasmic reticulum, which possesses excellent targeting to the endoplasmic reticulum, and provides a new design method for the establishment of other endoplasmic reticulum-targeting probes. In addition, the probe ER-N successfully realized the detection of endogenous and exogenous ONOO- in cells and zebrafish. More importantly, the probe ER-N was able to be used for tracing changes in the levels of ONOO- in the endoplasmic reticulum of cells and tissues under drug stimulation. Together, these results suggest that the probe ER-N has the potential to be a tool for DILI detection and new drug screening.

Abstract 4200: A novel 3D culture platform, E-slice, retains intact tumor microenvironment and tumor infiltrating immune cells

Abstract Human cancer tissue is a complex ecosystem with heterogeneous cell types that communicate with each other dynamically to promote tumor progression and immune evasion. Hence, experimental systems that destroy or select for cancer cells in culture have often failed to accurately predict clinical responses of new drugs. Therefore, a technology that can retain the intact tumor microenvironment (TME) and accurately predict cancer patient’s response to anti-cancer treatments, including immunotherapies, will greatly benefit patients and dramatically reduce the cost of new drug development. E-slice is a proprietary 3D human tumor culture platform that maintains individual patient tumor’s unique ecosystem and the TME ex vivo for up to 30 days. It is generated by making thin sections of intact, fresh tumor tissues and culturing them in serum-free, defined media. As such, immune components and the TME in E-slices faithfully recapitulate human tumors ex vivo. We demonstrate that E-slices can be used to: 1) measure viability changes upon anti-cancer agent treatment longitudinally; 2) retain the native TME and tissue architecture since E-slices are never dissociated or artificially reconstituted; 3) culture any solid tumor types from patient needle biopsy cores or surgical samples, PDX, or mouse models; 4) perform high content drug screening on human tissues; 5) discover secreted biomarkers and to 6) discover or validate molecular mechanisms of action or therapy resistance; and 7) measure immunotherapy responses ex vivo from human tissues. Importantly, it has been shown to accurately predict individual patient treatment responses to chemotherapies and targeted therapies in 4-8 days, paving the way for evidence-based personalized treatment selections in a clinically actionable time frame. Furthermore, single cell RNA-sequencing analysis demonstrates that E-slices can retain the viability and molecular phenotypes of tumor infiltrating immune cells for up to 8 days ex vivo, providing a unique platform to study human tumor-immune interactions and evaluate efficacy of immune modulators on human tumor tissues ex vivo. Citation Format: Viridiana Leyva-Aranda, Thomas Gallup, Jose Maldonado, Corina Margain, Sang Yun, David Gallup, Min Kim, Kyuson Yun. A novel 3D culture platform, E-slice, retains intact tumor microenvironment and tumor infiltrating immune cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4200.

Exploring the mechanism of contact-dependent cell-cell communication on chemosensitivity based on single-cell high-throughput drug screening platform

High-throughput drug screening (HTDS) has significantly reduced the time and cost of new drug development. Nonetheless, contact-dependent cell-cell communication (CDCCC) may impact the chemosensitivity of tumour cells. There is a pressing need for low-cost single-cell HTDS platforms, alongside a deep comprehension of the mechanisms by which CDCCC affects drug efficacy, to fully unveil the efficacy of anticancer drugs. In this study, we develop a microfluidic chip for single-cell HTDS and evaluate the molecular mechanisms impacted by CDCCC using quantitative mass spectrometry-based proteomics. The chip achieves high-quality drug mixing and single-cell capture, with single-cell drug screening results on the chip showing consistency with those on the 96-well plates under varying concentration gradients. Through quantitative proteomic analysis, we deduce that the absence of CDCCC in single tumour cells can enhance their chemoresistance potential, but simultaneously subject them to stronger proliferation inhibition. Additionally, pathway enrichment analysis suggests that CDCCC could impact several signalling pathways in tumour single cells that regulate vital biological processes such as tumour proliferation, adhesion, and invasion. These results offer valuable insights into the potential connection between CDCCC and the chemosensitivity of tumour cells. This research paves the way for the development of single-cell HTDC platforms and holds the promise of advancing tumour personalized treatment strategies.

Predicting Drug-Target Affinity by Learning Protein Knowledge From Biological Networks.

Predicting drug-target affinity (DTA) is a crucial step in the process of drug discovery. Efficient and accurate prediction of DTA would greatly reduce the time and economic cost of new drug development, which has encouraged the emergence of a large number of deep learning-based DTA prediction methods. In terms of the representation of target proteins, current methods can be classified into 1D sequence- and 2D-protein graph-based methods. However, both two approaches focused only on the inherent properties of the target protein, but neglected the broad prior knowledge regarding protein interactions that have been clearly elucidated in past decades. Aiming at the above issue, this work presents an end-to-end DTA prediction method named MSF-DTA (Multi-Source Feature Fusion-based Drug-Target Affinity). The contributions can be summarized as follows. First, MSF-DTA adopts a novel "neighboring feature"-based protein representation. Instead of utilizing only the inherent features of a target protein, MSF-DTA gathers additional information for the target protein from its biologically related "neighboring" proteins in PPI (i.e., protein-protein interaction) and SSN (i.e., sequence similarity) networks to get prior knowledge. Second, the representation was learned using an advanced graph pre-training framework, VGAE, which could not only gather node features but also learn topological connections, therefore contributing to a richer protein representation and benefiting the downstream DTA prediction task. This study provides new perspective for the DTA prediction task, and evaluation results demonstrated that MSF-DTA obtained superior performances compared to current state-of-the-art methods.

Molecular Docking Studies of Rifampicin - rpoB complex: Repurposing Drug Design Implications for against Plasmodium falciparum Malaria through a Computational Approach.

Malaria is one of the world's most devastating diseases, infecting well over 300 million people annually and killing between 2 and 3 million worldwide. Increasing parasite resistance to many existing drugs is exacerbating disease. Resistance to commonly used malarial drugs is increasing the need to develop new drugs urgently. Due to the slow pace and substantial costs of new drug development, repurposing of old drugs which is recently increasingly becoming an attractive proposition of highly efficient and effective way of drug discovery led us to study the drug rifampicin for this purpose. The present paper aims to investigate the route of Plasmodium falciparum apicoplast-targeted proteins that putatively encode β subunits of RNA polymerase with an objective to develop an effective antimalarial drug. Homology searching for conserved binding site to the rifampicin drug and the functional analysis of rpoB gene were done. Multiple Sequence alignment analysis of rpoB was compared with that in E.coli - rpoB and M. tuberculosis - rpoB. Docking studies of Rifampicin - rpoB complex was also done for finding binding affinity. The results of computational studies showed that rifampicin is a potential drug for malaria.

Repurposing Marketed Drugs as Antidepressants with In Silico, In Vivo, and In Vitro Screening Methods.

Drug repurposing is a strategy to identify new therapeutic uses for marketed drugs, discontinued or shelved drugs, and drug candidates in clinical development. Drug repurposing has gained momentum over the past few years. A slow rate of new drug discovery and higher cost of new drug development attracted the attention for repurposing and repositioning of old medications . A deeper understanding of the pathogenesis of depression encourages novel discoveries through drug repurposing to treat depression. In this study, indole- and isatin-containing drugs are going to be repurposed using computational methods for the treatment of depression.

Abstract 10912: Sarpogrelate, an Antiplatelet Agent, Suppressed Cardiac Hypertrophy and Systolic Dysfunction in a 5-ht 2a Receptor-Independent Manner

Introduction: Recently, the cost of new drug development is increasing year by year, and drug repositioning is being used as a strategy for developing new treatments by saving time and costs. We used a library of approved drugs to screen for compounds that suppress cardiomyocyte hypertrophy and identified the antiplatelet drug sarpogrelate, a selective serotonin-2A receptor antagonist, as a candidate for heart failure therapy. In this study, we investigated the effect of sarpogrelate on cultured cardiomyocyte hypertrophy and development of heart failure. Methods & Results: First, primary cultured cardiomyocytes were treated with 1 μM sarpogrelate and then stimulated with various hypertrophic stimuli (30 μM phenylephrine (PE), 0.1 μM angiotensin II and 0.1 μM endothelin 1). Sarpogrelate repressed hypertrophic responses induced by each stimulus, such as myofibrillar organization and increase in cell size. Moreover, PE-induced transactivation of the hypertrophic response genes promoter was significantly inhibited by sarpogrelate. Western blotting (WB) showed that sarpogrelate significantly suppressed PE-induced the phosphorylation of ERK1/2 and GATA4. Next, C57BL/6j male mice were subjected to a transverse aortic constriction (TAC) and sham operation. One day after the operation, the mice were randomly divided into 3 groups: sarpogrelate at 1 mg/kg or 5 mg/kg, and vehicle as a control. Daily oral administration was repeated for 8 weeks. Echocardiographic analysis showed that 5 mg/kg sarpogrelate significantly prevented a TAC-induced increase in posterior left ventricular wall thickness and a decrease in fractional shortening at 8 weeks after the operation. Five mg/kg sarpogrelate also suppressed a TAC-induced increase in HW/BW ratio, myocardial cell diameter, perivascular fibrosis, and mRNA levels of ANF and BNP. Moreover, the WB analysis showed that 5 mg/kg sarpogrelate significantly suppressed TAC-induced phosphorylation of the ERK1/2 and GATA4. Conclusions: Sarpogrelate significantly suppresses cardiomyocyte hypertrophy and the development of heart failure via at least, in part, by inhibition of ERK1/2-GATA4 pathway. These findings suggest that sarpogrelate may be an effective agent for heart failure therapy.

Abstract P352: An Antiplatelet Agent Sarpogrelate Suppresses Pressure Overload-induced Development Of Heart Failure In A 5-ht 2a Receptor-independent Manner

Purpose: The cost of new drug development is increasing year by year, and drug repositioning is being used as a strategy to develop new treatments at low-cost. We used a library of approved drugs to screen for compounds that suppress cardiomyocyte hypertrophy, and identified as a candidate the antiplatelet drug sarpogrelate, a selective serotonin-2A (5-HT 2A ) receptor antagonist. In this study, we examined the effect of sarpogrelate on cultured cardiomyocyte hypertrophy and development of heart failure. Methods & Results: First, primary cultured cardiomyocytes were treated with 1 μM sarpogrelate and then stimulated with various hypertrophic stimuli (30 μM phenylephrine (PE), 0.1 μM angiotensin II and 0.1 μM endothelin 1). The results of immunofluorescence staining with anti-MHC antibody showed that sarpogrelate significantly suppressed cardiomyocyte hypertrophy induced by each stimulus. Western blotting and qPCR analysis showed that the mRNA and protein levels of 5-HT 2A receptor did not change by PE, and sarpogrelate significantly suppressed PE-induced phosphorylation of ERK1/2 and GATA4. Next, C57BL/6j male mice were subjected to a transverse aortic constriction (TAC) and sham operation. One day after the operation, the mice were randomly divided into 3 groups: sarpogrelate at 1 mg/kg or 5 mg/kg, and vehicle as a control. Daily oral administration was repeated for 8 weeks. Echocardiographic analysis showed that 5 mg/kg sarpogrelate significantly prevented a TAC-induced increase in posterior left ventricular wall thickness and a decrease in fractional shortening at 8 weeks after the operation. Five mg/kg sarpogrelate also suppressed TAC-induced increase in HW/BW ratio, cross-sectional areas, perivascular fibrosis, and mRNA levels of ANF and BNP. Moreover, the western blotting analysis showed that 5 mg/kg sarpogrelate significantly suppressed TAC-induced phosphorylation of ERK1/2. Conclusions: These results indicate that sarpogrelate significantly suppresses cardiomyocyte hypertrophy and the development of heart failure via at least, in part, by inhibition of ERK1/2-GATA4 pathway. These findings suggest that sarpogrelate may be an effective agent for heart failure therapy.

Drug Repurposing for the Management of Depression: Where Do We Stand Currently?

A slow rate of new drug discovery and higher costs of new drug development attracted the attention of scientists and physicians for the repurposing and repositioning of old medications. Experimental studies and off-label use of drugs have helped drive data for further studies of approving these medications. A deeper understanding of the pathogenesis of depression encourages novel discoveries through drug repurposing and drug repositioning to treat depression. In addition to reducing neurotransmitters like epinephrine and serotonin, other mechanisms such as inflammation, insufficient blood supply, and neurotoxicants are now considered as the possible involved mechanisms. Considering the mentioned mechanisms has resulted in repurposed medications to treat treatment-resistant depression (TRD) as alternative approaches. This review aims to discuss the available treatments and their progress way during repositioning. Neurotransmitters’ antagonists, atypical antipsychotics, and CNS stimulants have been studied for the repurposing aims. However, they need proper studies in terms of formulation, matching with regulatory standards, and efficacy.

Generative chemistry: drug discovery with deep learning generative models.

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.

MGCN: Graph Convolutional Network for Drug Molecular Screening

The intersection of drug research and development and neural network research is getting closer, new methods and new technologies are emerging one after another, using neural network to improve the efficiency of drug research and development, and rapidly innovate the treatment of diseases. In drug development, one target protein corresponds to multiple drug molecules. In order to save the time and cost of new drug development, virtual screening of key steps in new drug development is studied. A virtual screening model for drug molecules is proposed, and the graph convolutional neural network is used to classify drug molecules to achieve the purpose of preliminary screening. The graph convolutional network model is constructed based on DGL and combined with cross-entropy and fully connected networks. The model is carried out by experimental means analysis. The experimental results show that the graph neural network can complete the classification task of drug molecules. Through the verification of molecular docking software, the screened molecules are real and effective, reducing the time spent on virtual screening, and it has become an inspiration for further exploration of graph neural networks in drug development.

A combined high-throughput and high-content platform for unified on-chip synthesis, characterization and biological screening

Acceleration and unification of drug discovery is important to reduce the effort and cost of new drug development. Diverse chemical and biological conditions, specialized infrastructure and incompatibility between existing analytical methods with high-throughput, nanoliter scale chemistry make the whole drug discovery process lengthy and expensive. Here, we demonstrate a chemBIOS platform combining on-chip chemical synthesis, characterization and biological screening. We developed a dendrimer-based surface patterning that enables the generation of high-density nanodroplet arrays for both organic and aqueous liquids. Each droplet (among > 50,000 droplets per plate) functions as an individual, spatially separated nanovessel, that can be used for solution-based synthesis or analytical assays. An additional indium-tin oxide coating enables ultra-fast on-chip detection down to the attomole per droplet by matrix-assisted laser desorption/ionization mass spectrometry. The excellent optical properties of the chemBIOS platform allow for on-chip characterization and in-situ reaction monitoring in the ultraviolet, visible (on-chip UV-Vis spectroscopy and optical microscopy) and infrared (on-chip IR spectroscopy) regions. The platform is compatible with various cell-biological screenings, which opens new avenues in the fields of high-throughput synthesis and drug discovery.

Methodological considerations in the design of clinical trial for innovative hepatitis B drugs

The research and development of innovative drugs targeting the clinical cure of chronic hepatitis B has become active in recent years. In the clinical trials of new drugs for hepatitis B, the use of new design concepts, new methods, and new technologies to evaluate the efficacy of innovative drugs is expected to shorten the clinical research process of candidate new drugs and reduce the cost of new drug development. However, the new designs such as seamless adaptive design and master plan design have few practical applications in clinical trials of innovative hepatitis B drugs. This article will focus on the methodological factors in the design of phase II/III clinical trials of innovative hepatitis B drugs, and introduce the key points of the new clinical trial design in order to provide methodological references for the development of innovative hepatitis B drugs.

Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature

Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature

Is antidyslipidemic statin use for cancer prevention a promising drug repositioning approach?

Novel pharmacological therapies are in development for cancer, ranging from conventional chemotherapeutic drugs to molecular targeted drugs, antibody-based drugs, and immune checkpoint inhibitors, which are developed using new technologies. However, the increasing cost of new drug development is increasing the costs of national healthcare and putting pressure on government finances worldwide. Under these circumstances, drug repositioning (i.e. discovering novel effects of existing drugs, thereby allowing their use to treat other diseases) has become a major focus because of reliability and cost reduction. It is becoming increasingly clear that statins (currently used for treating dyslipidemia) can be effective in the prevention of coronary disease, heart failure, and arrhythmia. Epidemiological as well as basic research studies and epidemiological surveys have showed that statins have a suppressive effect on cancers and that they have an antitumor effect on colorectal, prostate, breast, cervical, endometrial, and ovarian cancers. Given the pharmacological mechanism of action of statins, they may have an antitumor effect on cancer types in which the mevalonate pathway is activated as well as on tumors with p53 mutations. To investigate this further, it would be necessary to conduct a large-scale survey after confirming the clinical background of patients as well as their mutational status, and therefore, great hope has been placed on the role of academia and public institutions. Thus, there is an urgent need for researchers to be actively involved in investigator-initiated clinical trials.

Immunotherapy with check-point inhibitors (CPI) in adult malignancies: a protocol for the systematic review of the quality of economic analyses

BackgroundImmuno-oncology, and in particular, check-point inhibitors (CPIs), have led to a paradigm shift in the field of cancer care. The cost of new drug development is high, and many novel agents in oncology are significantly more expensive than older agents. Therefore, healthcare funders have factored measures of cost-effectiveness into decisions concerning drug reimbursement and incorporation of new agents into treatment algorithms. The methodology of cost-effectiveness evaluations, however, is less rigorously applied than those evaluating clinical efficacy and safety data. Thus, in spite of many regulatory bodies having approved CPIs based on existing economic analyses, to date, there has not been a systematic evaluation of the quality of health economic studies conducted on this new class of agents.Therefore, we propose to systematically review the methodologic and reporting quality of cost-effectiveness and cost-utility studies assessing CPIs to alternate established therapies, other immuno-oncology regimens, or placebo, in adults with malignancies.Methods/designThe systematic review will include all published economic evaluations of CPIs compared with at least one other treatment in adult patients with solid or hematologic malignancies. A search will be performed to identify relevant studies in Ovid MEDLINE, EMBASE, Cost-effectiveness Analysis Registry, Evidence-Based Medicine Reviews, and the NIHR-HTA database. The titles and abstracts of all identified studies will be independently reviewed by two reviewers, who will then assess the full text of all articles deemed to meet eligibility criteria. Assessed articles will be screened for compliance with the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) criteria. The association, with CHEERS criteria, of the journal impact factor, publication year, funding source, tumor site, trial or model-based study, and CPIs studied, will then be assessed.DiscussionThe systematic review will aim to provide an overview of the quality of economic analyses evaluating CPIs for the treatment of malignancies in adult patients. Any systemic or recurrent deficiencies in methodological or reporting quality will be described and used to inform recommendations for improved reporting of economic analyses.Systematic review registrationThis review will not be registered with PROSPERO, it does not meet the eligibility criterion of addressing an outcome of the direct patient or clinical relevance.

Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing.

The cost of new drug development has been increasing, and repurposing known medications for new indications serves as an important way to hasten drug discovery. One promising approach to drug repositioning is to take advantage of machine learning (ML) algorithms to learn patterns in biological data related to drugs and then link them up to the potential of treating specific diseases. Here we give an overview of the general principles and different types of ML algorithms, as well as common approaches to evaluating predictive performances, with reference to the application of ML algorithms to predict repurposing opportunities using drug expression data as features. We will highlight common issues and caveats when applying such models to repositioning. We also introduce resources of drug expression data and highlight recent studies employing such an approach to repositioning.

The risk of innovation: measuring drug clinical development in Brazil

Although risk is inherent in innovation, the pharmaceutical sector still leads research and development (R&D) investments. Therefore, the awareness of potential risks is especially important for this sector that spends approximately 70% of the total cost of new drug development on clinical trials. The economic crisis in Europe and its reimbursement system has made emerging countries, like Brazil, potential strategic markets for the return of these high investments. Using a public database (ClinicalTrials.gov), this study estimated the probabilities of failure and success of clinical trials in Brazil up to approval by the regulatory authority. The results showed the risk in conducting phase 3 clinical trials, the most extensive and complex phase, is considered high (26.7%). On the other hand, regulatory approval represents a high success rate, since over 90% of the drugs with positive results in phase 3 were approved by the Brazilian Agency.

The risk of innovation: measuring drug clinical development in Brazil

Although risk is inherent in innovation, the pharmaceutical sector still leads research and development (R&D) investments. Therefore, the awareness of potential risks is especially important for this sector that spends approximately 70% of the total cost of new drug development on clinical trials. The economic crisis in Europe and its reimbursement system has made emerging countries, like Brazil, potential strategic markets for the return of these high investments. Using a public database (ClinicalTrials.gov), this study estimated the probabilities of failure and success of clinical trials in Brazil up to approval by the regulatory authority. The results showed the risk in conducting phase 3 clinical trials, the most extensive and complex phase, is considered high (26.7%). On the other hand, regulatory approval represents a high success rate, since over 90% of the drugs with positive results in phase 3 were approved by the Brazilian Agency.