Approval success rates of drug candidates based on target, action, modality, application, and their combinations

Yamaguchi, Shingo; Kaneko, Masayuki; Narukawa, Mamoru

doi:10.1111/cts.12980

Cited by 60 publications

(51 citation statements)

References 28 publications

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“…Phase I trials are typically performed after pre-clinical studies have suggested the potential utility of an investigational medication for a certain disease. However, <10% of medications entering phase I clinical testing will achieve FDA approval and reach the market ( 28 , 29 ). This has motivated the development of computational methods to reduce risk and increase efficiency of novel drug development.…”

Section: Discussionmentioning

confidence: 99%

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Ravanmehr

Blau

Cappelletti

et al. 2021

NAR Genomics and Bioinformatics

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Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

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Section: Discussionmentioning

confidence: 99%

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Ravanmehr

Blau

Cappelletti

et al. 2021

NAR Genomics and Bioinformatics

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“…Specifically, studies using ML and quantitative modeling have been widely applied to clinical trials with varying goals and using diverse types of independent predictive variables. However, the annotation of clinical trial datasets is highly time-consuming and require domain expertise, due to which nearly all outcome prediction studies use commercially available, annotated clinical trial datasets (Lo et al, 2018, Feijoo et al, 2020, Zhavoronkov et al, 2020, Wong et al, 2019, Vergetis et al, 2020, Yamaguchi et al, 2021. A labyrinth set of studies evaluate various factors that affect trial outcomes and span a wide set of questions (see Feijoo et al, 2020 for a detailed review of ML and non-ML research).…”

Section: Machine Learning To Predict Clinical Trial Outcomesmentioning

confidence: 99%

“…A labyrinth set of studies evaluate various factors that affect trial outcomes and span a wide set of questions (see Feijoo et al, 2020 for a detailed review of ML and non-ML research). Yamaguchi et al (Yamaguchi et al, 2021), apart from implementing a logistic regression model that uses target, mechanism of action (MOA), modality, and application of drugs as features to predict clinical trial success, also provide a review that classifies literature based on the type of features used to predict clinical outcomes. We summarize the principal differentiation in Section 1.1.1 and refer the reader to the two recent studies (Feijoo et al, 2020, Yamaguchi et al, 2021 that provide exhaustive reviews of predictive algorithms.…”

Section: Machine Learning To Predict Clinical Trial Outcomesmentioning

confidence: 99%

“…Starting with a big positive shift away from the dip in approvals between 2015-17 (Dowden & Munro, 2019), the data published by FDA (the annual New Drug Approvals Report) suggest that an average of 46 drugs have been approved per year across the past 5 years. Nonetheless, the success rate of molecules to transition from Phase II to III is the least (Yamaguchi et al, 2021), and hence exemplifies the phase transition that carries the highest risk. Further, studies (Dowden & Munro, 2019) indicate that there are higher chances of a molecule being launched as a drug when it reaches Phase III.…”

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confidence: 99%

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Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Athri

Murali

Muralidhar

et al. 2022

Preprint

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The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications to costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-related features, and NLP-based compound representation. Biological activities have never been used as a predictive feature. We have extracted the drug-disease pair from clinical trials and mapped target(s) to that pair using multiple data sources. Empirical results demonstrate that ensemble learning outperforms independently trained, small-data ML models. We report results and inferences derived from a Random forest classifier with an average accuracy of 93%, and an F1 score of 0.96 for the 'Pass' class. 'Pass' refers to one of the two classes (Pass/ Fail) of all clinical trials and the model performed well in predicting the 'Pass' category. An analysis of the features demonstrates that bioactivity plays an important role in predicting the outcome of a clinical trial. A significant effort has gone into the production of the dataset that, for the first time, integrates clinical trial information with protein targets. All code to map these entities is available through this study, and all data are from publicly available sources. While our model identifies low-lying inferences when biological activities are included, the code to integrate biological activity and target information provide researchers with access to deep curated and proprietary clinical trial databases the ability to get deeper insights, better statistical significance, and capabilities to better predict trial failures.

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“…In traditional drug discovery, the average period of new drug development pipelines takes at least 12 years from the initial discovery to the marketplace [ 23 ]. Although the pharmaceutical industry invested 83 billion USD worldwide on research and development (R&D) expenditures in 2019 [ 24 ], the success rate of a drug candidate starting from clinical trial to marketing approval was approximately 10~20%, which has not changed for the past few decades [ 25 ]. On the contrary, drug repurposing (also known as drug repositioning), which aims to identify new therapeutic uses of approved or investigational drugs, is a feasible and advantageous strategy with a lower development risk and time cost.…”

Section: Introductionmentioning

confidence: 99%

Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications

Ting

Chen

2022

IJMS

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The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world at a rapid speed. Among COVID-19 patients, SARS-CoV-2-associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS are quite common, and their therapeutic treatments are limited because the intricated pathophysiology having been not fully understood. In this study, to investigate the pathogenic mechanism of SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS, first, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining. With the help of host-pathogen RNA sequencing (RNA-Seq) data, real HPI-GWGEN of COVID-19-associated ARDS and non-viral ARDS were obtained by system modeling, system identification, and Akaike information criterion (AIC) model order selection method to delete the false positives in candidate HPI-GWGEN. For the convenience of mitigation, the principal network projection (PNP) approach is utilized to extract core HPI-GWGEN, and then the corresponding core signaling pathways of COVID-19-associated ARDS and non-viral ARDS are annotated via their core HPI-GWGEN by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-associated ARDS and non-viral ARDS, we identified essential biomarkers as drug targets of pathogenesis by comparing the core signal pathways between COVID-19-associated ARDS and non-viral ARDS. The deep neural network of the drug–target interaction (DNN-DTI) model could be trained by drug–target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates to repurpose potential multiple-molecule drugs by the filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity, and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-associated ARDS and non-viral ARDS but also provide novel therapeutic options for COVID-19-associated ARDS and non-viral ARDS.

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Approval success rates of drug candidates based on target, action, modality, application, and their combinations

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 60 publications

References 28 publications

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications

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