Success Rate Of Drug Development Research Articles

Systematic Mendelian Randomization Exploring Druggable Genes for Hemorrhagic Strokes.

Patients with hemorrhagic stroke have high rates of morbidity and mortality, and drugs for prevention are very limited. Mendelian randomization (MR) analysis can increase the success rate of drug development by providing genetic evidence. Previous MR analyses only analyzed the role of individual drug target genes in hemorrhagic stroke; therefore, we used MR analysis to systematically explore the druggable genes for hemorrhagic stroke. We sequentially performed summary-data-based MR analysis and two-sample MR analysis to assess the associations of all genes within the database with intracranial aneurysm, intracerebral hemorrhage, and their subtypes. Validated genes were further analyzed by colocalization. Only genes that were positive in all three analyses and were druggable were considered desirable genes. We also explored the mediators of genes affecting hemorrhagic stroke incidence. Finally, the associations of druggable genes with other cardiovascular diseases were analyzed to assess potential side effects. We identified 56 genes that significantly affected hemorrhagic stroke incidence. Moreover, TNFSF12, SLC22A4, SPARC, KL, RELT, and ADORA3 were found to be druggable. The inhibition of TNFSF12, SLC22A4, and SPARC can reduce the risk of intracranial aneurysm, subarachnoid hemorrhage, and intracerebral hemorrhage. Gene-induced hypertension may be a potential mechanism by which these genes cause hemorrhagic stroke. We also found that blocking these genes may cause side effects, such as ischemic stroke and its subtypes. Our study revealed that six druggable genes were associated with hemorrhagic stroke, and the inhibition of TNFSF12, SLC22A4, and SPARC had preventive effects against hemorrhagic strokes.

ADSS: A Composite Score to Detect Disease Progression in Alzheimer's Disease.

Composite scores have been increasingly used in trials for Alzheimer's disease (AD) to detect disease progression, such as the AD Composite Score (ADCOMS) in the lecanemab trial. To develop a new composite score to improve the prediction of outcome change. We proposed to develop a new composite score based on the statistical model in the ADCOMS, by removing duplicated sub-scales and adding the model selection in the partial least squares (PLS) regression. The new AD composite Score with variable Selection (ADSS) includes 7 cognitive sub-scales. ADSS can increase the sensitivity to detect disease progression as compared to the existing total scores, which leads to smaller sample sizes using the ADSS in trial designs. ADSS can be utilized in AD trials to improve the success rate of drug development with a high sensitivity to detect disease progression in early stages.

Compound Absorption in Polymer Devices Impairs the Translatability of Preclinical Safety Assessments.

Organotypic and microphysiological systems (MPS) that can emulate the molecular phenotype and function of human tissues, such as liver, are increasingly used in preclinical drug development. However, despite their improved predictivity, drug development success rates have remained low with most compounds failing in clinical phases despite promising preclinical data. Here, it is tested whether absorption of small molecules to polymers commonly used for MPS fabrication can impact preclinical pharmacological and toxicological assessments and contribute to the high clinical failure rates. To this end, identical devices are fabricated from eight different MPS polymers and absorption of prototypic compounds with different physicochemical properties are analyzed. It is found that overall absorption is primarily driven by compound hydrophobicity and the number of rotatable bonds. However, absorption can differ by >1000-fold between polymers with polydimethyl siloxane (PDMS) being most absorptive, whereas polytetrafluoroethylene (PTFE) and thiol-ene epoxy (TEE) absorbed the least. Strikingly, organotypic primary human liver cultures successfully flagged hydrophobic hepatotoxins in lowly absorbing TEE devices at therapeutically relevant concentrations, whereas isogenic cultures in PDMS devices are resistant, resulting in false negative safety signals. Combined, these results can guide the selection of MPS materials and facilitate the development of preclinical assays with improved translatability.

Artificial Intelligence in Drugs Discovery and Development

Artificial intelligence (AI) has the potential to transform drug discovery and development by accelerating the identification of new drug targets, improving the efficiency of drug discovery processes, and enabling personalized medicine. AI can help by analyzing large amounts of data, such as genetic data, protein structures, and disease pathways, to identify new drug targets. This can significantly speed up the drug discovery process and increase the success rate of drug development.

The Role of Oxytocin and Vasopressin in Drug-Induced Reward-Implications for Social and Non-Social Factors.

Drug abuse is a worldwide problem that leads to negative physical, mental, and economic consequences. Although pharmacological strategies for drug addiction management have been widely studied, therapeutic options with high efficacy and a low side-effects profile are still limited. Recently, there has been a growing interest in oxytocin (OT) and vasopressin (AVP) systems as potential therapeutic targets for the treatment of drug abuse. OT and AVP are hypothalamic neuropeptides involved in numerous physiological processes. Additionally, studies show that these neurohormones are highly implicated in the modulation of a wide range of behaviors. Interestingly, ample evidence has shown that both, OT and AVP are able to decrease the consumption of different drugs of abuse, as well as to ameliorate their rewarding and reinforcing effects. Furthermore, OT and AVP have been strongly involved in prosocial effects and social reward. In particular, OT has been shown to be able to shift drug-induced reward into social-induced reward, mainly due to its interaction with the dopaminergic system. This phenomenon is also reflected in the results of clinical trials where intranasal OT shows promising efficacy in managing substance use disorder. Therefore, the aim of this review is to comprehensively characterize the involvement of OT and AVP in the rewarding and other behavioral effects of drugs of abuse in animal models, with a particular highlight on the impact of social factors on the observed effects. Understanding this relationship may contribute to higher drug development success rates, as a result of a more profound and deliberate studies design.

Deep learning approaches for <italic>de novo</italic> drug design: an overview

<p indent="0mm">The design and discovery of lead compounds is the most challenging and creative stage in drug development, and multiple attributes of candidate molecules need to be optimized in this process, such as structural novelty, biological activity, target selectivity, synthesability, druggability, and safety. Although the development and application of computer-aided drug design methods have substantially reduced the time and cost in the lead compound discovery stage, it has not yet been able to reverse the current situation of low success rate of drug development. In recent years, with the continuous development of deep learning (DL) technology, DL-based <italic>de novo</italic> drug design methods have brought new opportunities for the discovery of lead compounds. The DL frameworks used by these new drug design models include encoder-decoder, recurrent neural network, generative adversarial networks, and reinforcement learning. In this paper, the basic theory, the input molecular representation and the evaluation metrics of their methods are reviewed, and the application prospect of DL-based <italic>de novo</italic> drug design methods is also discussed.

Predictiveness of the Human-CYP3A4-Transgenic Mouse Model (Cyp3aXAV) for Human Drug Exposure of CYP3A4-Metabolized Drugs.

The extrapolation of drug exposure between species remains a challenging step in drug development, contributing to the low success rate of drug approval. As a consequence, extrapolation of toxicology from animal models to humans to evaluate safe, first-in-human (FIH) doses requires high safety margins. We hypothesized that a human-CYP3A4-expressing transgenic (Cyp3aXAV) mouse is a more predictive model for human drug exposure of CYP3A4-metabolized small-molecule drugs. Population pharmacokinetic models based on wild-type (WT) and Cyp3aXAV mouse pharmacokinetic data of oral lorlatinib, brigatinib, ribociclib and fisogatinib were allometrically scaled and compared to human exposure. Extrapolation of the Cyp3aXAV mouse model closely predicted the observed human exposure for lorlatinib and brigatinib with a 1.1-fold and 1.0-fold difference, respectively, compared to a 2.1-fold and 1.9-fold deviation for WT-based extrapolations of lorlatinib and brigatinib, respectively. For ribociclib, the extrapolated WT mouse model gave better predictions with a 1.0-fold deviation compared to a 0.3-fold deviation for the extrapolated Cyp3aXAV mouse model. Due to the lack of a human population pharmacokinetic model for fisogatinib, only median maximum concentration ratios were calculated, resulting in ratios of 1.0 and 0.6 for WT and Cyp3aXAV mice extrapolations, respectively. The more accurate predictions of human exposure in preclinical research based on the Cyp3aXAV mouse model can ultimately result in FIH doses associated with improved safety and efficacy and in higher success rates in drug development.

Patient-Derived Lung Tumoroids-An Emerging Technology in Drug Development and Precision Medicine.

Synthetic 3D multicellular systems derived from patient tumors, or tumoroids, have been developed to complete the cancer research arsenal and overcome the limits of current preclinical models. They aim to represent the molecular and structural heterogeneity of the tumor micro-environment, and its complex network of interactions, with greater accuracy. They are more predictive of clinical outcomes, of adverse events, and of resistance mechanisms. Thus, they increase the success rate of drug development, and help clinicians in their decision-making process. Lung cancer remains amongst the deadliest of diseases, and still requires intensive research. In this review, we analyze the merits and drawbacks of the current preclinical models used in lung cancer research, and the position of tumoroids. The introduction of immune cells and healthy regulatory cells in autologous tumoroid models has enabled their application to most recent therapeutic concepts. The possibility of deriving tumoroids from primary tumors within reasonable time has opened a direct approach to patient-specific features, supporting their future role in precision medicine.

Precision Promise (PrP): An adaptive, multi-arm registration trial in metastatic pancreatic ductal adenocarcinoma (PDAC).

TPS4188 Background: The success rate of drug development in PDAC is disappointingly low.PrP is a transformative, adaptive platform clinical trial designed to continuously evaluate many novel therapeutic options while increasing the probability that patients (pts) are randomized to effective experimental therapies. It cultivates enhanced cooperation among groups representing pts advocacy, pharmaceutical companies, academia, and the FDA. This patient-centric study aims to become the largest Phase 3 registrational study in PDAC and represents a fundamental shift in drug development for PDAC in the United States (US). Methods: PrP (NCT04229004) is a platform clinical trial sponsored by the Pancreatic Cancer Action Network (PanCAN), developed based on the FDA 2020 guidance document regarding "complex innovative designs" in registration trials https://www.fda.gov/media/130897/download . It utilizes adaptive randomization and other Bayesian statistical innovations provided by Berry Consultants LLC, including the “time machine” which uses all previously randomized controls for each arm, suitably adjusted for line of therapy and the time period of the arm. Focused on 1st and 2nd line treatment of mPDAC, PrP uses an adaptive platform design with randomization to one of 2 control arms (gemcitabine + nab-paclitaxel (GA) or mFOLFIRINOX, 30% of pts) or experimental therapy (70% of pts). Candidate experimental arms are reviewed by an Arm Selection Committee based on validity of the treatment target and strength of the pre-clinical and clinical data. The primary endpoint is overall survival (OS). Pts undergo pre- and on-treatment biopsies with state-of-the-art genomic, transcriptomic, and immune analysis, along with a serial collection of blood-based research samples. Pts are managed using novel supportive care techniques; PrP contains 3 sub-protocols evaluating quality of life, sarcopenia, and actigraphy. PrP launched in 2020 and has enrolled &gt; 130 pts; 30 US sites have been selected with 17 currently active. Current experimental arms include: (i) GA + Pamrevlumab, an anti-CTGF Ab, (ii) Racemetyrosine monotherapy, a cancer metabolism-based therapy (for follow-up of patients) and (iii) an immuno-oncology arm in activation. Other arms are in the planning stages. Compared to traditional designs, PrP offers several advantages: multiple investigational treatments evaluated in parallel over time; ̃175 pts per experimental arm required to initiate a regulatory registration; and continuous learning from every patient, resulting in significant savings of time and resources. PrP has created an entirely new learning environment for accelerating drug development in PDAC. Clinical trial information: NCT04229004.

Artificial intelligence-based decision support model for new drug development planning

New drug development guarantees a very high return on success, but the success rate is extremely low. Pharmaceutical companies have attempted to use various strategies to increase the success rate of drug development, but this goal has been difficult to achieve. In this study, we developed a model that can guide effective decision-making at the planning stage of new drug development by leveraging machine learning. The Drug Development Recommendation (DDR) model, we present here, is a hybrid model for recommending and/or predicting drug groups suitable for development by individual pharmaceutical companies. It combines association rule learning, collaborative filtering, and content-based filtering approaches for enterprise-customized recommendations. In the case of content-based filtering applying a random forest classification algorithm, the accuracy and area under curve were 78% and 0.74, respectively. In particular, the DDR model was applied to predict the success probability of companies developing Coronavirus disease 2019 (COVID-19) vaccines. It was demonstrated that the higher the predicted score from the DDR model, the more progress in the clinical phase of the COVID-19 vaccine development. Although our approach has limitations that should be improved, it makes scientific as well as industrial contributions in that the DDR model can support rational decision-making prior to initiating drug development by considering not only technical aspects but also company-related variables.

Engineering a Vascularized 3D Hybrid System to Model Tumor-Stroma Interactions in Breast Cancer.

The stromal microenvironment of breast tumors, namely the vasculature, has a key role in tumor development and metastatic spread. Tumor angiogenesis is a coordinated process, requiring the cooperation of cancer cells, stromal cells, such as fibroblasts and endothelial cells, secreted factors and the extracellular matrix (ECM). In vitro models capable of capturing such complex environment are still scarce, but are pivotal to improve success rates in drug development and screening. To address this challenge, we developed a hybrid alginate-based 3D system, combining hydrogel-embedded mammary epithelial cells (parenchymal compartment) with a porous scaffold co-seeded with fibroblasts and endothelial cells (vascularized stromal compartment). For the stromal compartment, we used porous alginate scaffolds produced by freeze-drying with particle leaching, a simple, low-cost and non-toxic approach that provided storable ready-to-use scaffolds fitting the wells of standard 96-well plates. Co-seeded endothelial cells and fibroblasts were able to adhere to the surface, spread and organize into tubular-like structures. For the parenchymal compartment, a designed alginate gel precursor solution load with mammary epithelial cells was added to the pores of pre-vascularized scaffolds, forming a hydrogel in situ by ionic crosslinking. The 3D hybrid system supports epithelial morphogenesis in organoids/tumoroids and endothelial tubulogenesis, allowing heterotypic cell-cell and cell-ECM interactions, while presenting excellent experimental tractability for whole-mount confocal microscopy, histology and mild cell recovery for down-stream analysis. It thus provides a unique 3D in vitro platform to dissect epithelial-stromal interactions and tumor angiogenesis, which may assist in the development of selective and more effective anticancer therapies.

Hepatic transporter-mediated pharmacokinetic drug-drug interactions: Recent studies and regulatory recommendations.

Transporter-mediated drug-drug interactions are one of the major mechanisms in pharmacokinetic-based drug interactions and correspondingly affecting drugs' safety and efficacy. Regulatory bodies underlined the importance of the evaluation of transporter-mediated interactions as a part of the drug development process. The liver is responsible for the elimination of a wide range of endogenous and exogenous compounds via metabolism and biliary excretion. Therefore, hepatic uptake transporters, expressed on the sinusoidal membranes of hepatocytes, and efflux transporters mediating the transport from hepatocytes to the bile are determinant factors for pharmacokinetics of drugs, and hence, drug-drug interactions. In parallel with the growing research interest in this area, regulatory guidances have been updated with detailed assay models and criteria. According to well-established preclinical results, observed or expected hepatic transporter-mediated drug-drug interactions can be taken into account for clinical studies. In this paper, various methods including in vitro, in situ, in vivo, in silico approaches, and combinational concepts and several clinical studies on the assessment of transporter-mediated drug-drug interactions were reviewed. Informative and effective evaluation by preclinical tools together with the integration of pharmacokinetic modeling and simulation can reduce unexpected clinical outcomes and enhance the success rate in drug development.

Drug Repurposing Strategy (DRS): Emerging Approach to Identify Potential Therapeutics for Treatment of Novel Coronavirus Infection.

Drug repurposing is also termed as drug repositioning or therapeutic switching. This method is applied to identify the novel therapeutic agents from the existing FDA approved clinically used drug molecules. It is considered as an efficient approach to develop drug candidates with new pharmacological activities or therapeutic properties. As the drug discovery is a costly, time-consuming, laborious, and highly risk process, the novel approach of drug repositioning is employed to increases the success rate of drug development. This strategy is more advantageous over traditional drug discovery process in terms of reducing duration of drug development, low-cost, highly efficient and minimum risk of failure. In addition to this, World health organization declared Coronavirus disease (COVID-19) as pandemic globally on February 11, 2020. Currently, there is an urgent need to develop suitable therapeutic agents for the prevention of the outbreak of COVID-19. So, various investigations were carried out to design novel drug molecules by utilizing different approaches of drug repurposing to identify drug substances for treatment of COVID-19, which can act as significant inhibitors against viral proteins. It has been reported that COVID-19 can infect human respiratory system by entering into the alveoli of lung via respiratory tract. So, the infection occurs due to specific interaction or binding of spike protein with angiotensin converting enzyme-2 (ACE-2) receptor. Hence, drug repurposing strategy is utilized to identify suitable drugs by virtual screening of drug libraries. This approach helps to determine the binding interaction of drug candidates with target protein of coronavirus by using computational tools such as molecular similarity and homology modeling etc. For predicting the drug-receptor interactions and binding affinity, molecular docking study and binding free energy calculations are also performed. The methodologies involved in drug repurposing can be categorized into three groups such as drug-oriented, target-oriented and disease or therapy-oriented depending on the information available related to quality and quantity of the physico-chemical, biological, pharmacological, toxicological and pharmacokinetic property of drug molecules. This review focuses on drug repurposing strategy applied for existing drugs including Remdesivir, Favipiravir, Ribavirin, Baraticinib, Tocilizumab, Chloroquine, Hydroxychloroquine, Prulifloxacin, Carfilzomib, Bictegravir, Nelfinavir, Tegobuvir and Glucocorticoids etc to determine their effectiveness toward the treatment of COVID-19.

Current status of drug safety evaluation using zebrafish

In recent years, the success rate of drug development has declined, and along with it, R&D costs have continued to rise. The rate of discontinuation of drug development due to safety reasons remains unchanged from 20 years ago. Therefore, it is important to check the safety of candidate compounds early in drug discovery in order to improve drug discovery efficiency. Under such circumstances, each company is focusing on establishing a low-cost, high-precision, and high-throughput safety screening system. The zebrafish is expected as a new experimental animal that serves as a bridge between in vitro and in vivo, and the progress of research in the last 15 years has been remarkable. At present, zebrafish are becoming a major experimental animal in Japan. At the same time, the gap between ideal and reality began to be seen, and it was time to once again understand the characteristics of zebrafish and think about its usage. This paper summarizes the points to be noted in the screening using zebrafish and introduces the use for actual safety evaluation.

P518 Ulcerative colitis drug development success rates are higher than the industry average

Abstract Background Given the increasing costs of drug development, coupled with low success rates, there is a need to both develop better predictive models and elucidate causes of failure. However, for indications such as ulcerative colitis, a baseline does not presently exist. In this paper, we review trends in drug development and establish a baseline for ulcerative colitis. Methods We used BiomedTracker to obtain information about the number of compounds, which were in development for ulcerative colitis between 2005 and 2018. We counted the number of compounds, which had reached a particular phase, had been discontinued during this phase, had successfully passed it or had been still in this phase. Success rates were derived for the different phases of drug development for all 73 candidate drugs and divided by type of molecule and type of company. A success rate was calculated by dividing the number of drugs successfully completing a particular phase by the sum of the number of drugs which were discontinued during this phase and the number of compounds that completed positively a phase. The likelihood for approval (LOA) for each phase was calculated by multiplying the success rate for the respective phase with the success rates of next stages of development. Results The success rate in phase I was 96 %, in phase II 53 % and in phase III 76%, while the respective LOA was 0.39, 0.4 and 0.76. Table 1 demonstrates the success rates and LOA by type of compound(New molecular entity (NME), Biologic, non-NME) and type of company (big, mid-size, emerging). Conclusion The general industry success rates and LOA for phase I is 64%, for Phase 2 30%, and for phase 3 60% while the LOA from phase I is 10%. These data are published by Michael Hay and co-authors in Nature in January 2014. The success rates in UC drug development are therefore higher than the overall industry success rates.

Efficient Discovery of Ligands Targeting Poor Lipid-signaling Metabolic Enzymes, as Facilitated by Activity-based Protein Profiling

Despite a continuous increase in R&D spending on potential new medicines, the success rate of drug development has not improved. The pharmaceutical industry is now facing a major challenge. As a college student who was studying pharmaceutical sciences in Japan, I became passionate about developing a new technology that would allow us to efficiently discover novel drug targets and selective chemical ligands for these targets. This realization encouraged me to join the PhD program at The Scripps Research Institute (TSRI) in 2013, where I carried out thesis research focusing on ligand discovery for poorly characterized metabolic enzymes for lipid signaling under the guidance of Prof. Benjamin Cravatt. TSRI is a unique place where researchers with different backgrounds collaborate frequently to conduct highly interdisciplinary research with the goal of translating cutting-edge research into clinical use. In this column, I am sharing my experiences as a PhD student at TSRI. I hope this column will be a useful source of information for younger students considering going abroad for a PhD degree.

Improving the odds of drug development success through human genomics: modelling study

Lack of efficacy in the intended disease indication is the major cause of clinical phase drug development failure. Explanations could include the poor external validity of pre-clinical (cell, tissue, and animal) models of human disease and the high false discovery rate (FDR) in preclinical science. FDR is related to the proportion of true relationships available for discovery (γ), and the type 1 (false-positive) and type 2 (false negative) error rates of the experiments designed to uncover them. We estimated the FDR in preclinical science, its effect on drug development success rates, and improvements expected from use of human genomics rather than preclinical studies as the primary source of evidence for drug target identification. Calculations were based on a sample space defined by all human diseases – the ‘disease-ome’ – represented as columns; and all protein coding genes – ‘the protein-coding genome’– represented as rows, producing a matrix of unique gene- (or protein-) disease pairings. We parameterised the space based on 10,000 diseases, 20,000 protein-coding genes, 100 causal genes per disease and 4000 genes encoding druggable targets, examining the effect of varying the parameters and a range of underlying assumptions, on the inferences drawn. We estimated γ, defined mathematical relationships between preclinical FDR and drug development success rates, and estimated improvements in success rates based on human genomics (rather than orthodox preclinical studies). Around one in every 200 protein-disease pairings was estimated to be causal (γ = 0.005) giving an FDR in preclinical research of 92.6%, which likely makes a major contribution to the reported drug development failure rate of 96%. Observed success rate was only slightly greater than expected for a random pick from the sample space. Values for γ back-calculated from reported preclinical and clinical drug development success rates were also close to the a priori estimates. Substituting genome wide (or druggable genome wide) association studies for preclinical studies as the major information source for drug target identification was estimated to reverse the probability of late stage failure because of the more stringent type 1 error rate employed and the ability to interrogate every potential druggable target in the same experiment. Genetic studies conducted at much larger scale, with greater resolution of disease end-points, e.g. by connecting genomics and electronic health record data within healthcare systems has the potential to produce radical improvement in drug development success rate.

Photolithographic Fabrication of Semi 3-D Microstructures Composed of Flexible Hydrogel Sheet for in Vivo-like Cell Culture System

Insufficient reliability of current drug screening by cell-based assay has been one of the factors in the poor success rate in drug development. To improve the situation, we proposed a cell culture system using semi 3-D hydrogel microstructures as cell culture scaffolds. Because of its flexibility and permeability, the microstructure was expected to enhance the physiological function of cells. We developed a simple method of fabricating the unique hydrogel microstructures composed of hydroxypropyl cellulose through photolithography. Functionalization with poly(styrene-co-maleic anhydride) gained their cell adhesion, and it was demonstrated that several kinds of cells were incubated on the cell culture scaffolds.

Licofelone-DPPC Interactions: Putting Membrane Lipids on the Radar of Drug Development.

(1) Background: Membrane lipids have been disregarded in drug development throughout the years. Recently, they gained attention in drug design as targets, but they are still disregarded in the latter stages. Thus, this study aims to highlight the relevance of considering membrane lipids in the preclinical phase of drug development. (2) Methods: The interactions of a drug candidate for clinical use (licofelone) with a membrane model system made of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) were evaluated by combining Langmuir isotherms, Brewster angle microscopy (BAM), polarization-modulation infrared reflection-absorption spectroscopy (PM-IRRAS), and grazing-incidence X-ray diffraction (GIXD) measurements. (3) Results: Licofelone caused the expansion of the DPPC isotherm without changing the lipid phase transition profile. Moreover, licofelone induced the reduction of DPPC packing density, while increasing the local order of the DPPC acyl chains. (4) Conclusions: The licofelone-induced alterations in the structural organization of phosphatidylcholine monolayers may be related to its pharmacological actions. Thus, the combination of studying drug-membrane interactions with the pharmacological characterization that occurs in the preclinical stage may gather additional information about the mechanisms of action and toxicity of drug candidates. Ultimately, the addition of this innovative step shall improve the success rate of drug development.

Translational science as a key for improving success rate of drug development

Translational science as a key for improving success rate of drug development