Combination Drug Clinical Trial

2003 ◽  
pp. 210-213 ◽  
Author(s):  
H Hung
Keyword(s):  
Clinical Trial ◽  
Combination Drug ◽  
Drug Clinical Trial

Artemeter-Lumefantrine therapeutic efficacy, safety and plasma levels in patients with uncomplicated falciparum malaria from the Colombian Pacific región

Infectio ◽  
2018 ◽  
Vol 22 (4) ◽  
pp. 199
Author(s):  
Alberto Tobón-Castaño ◽  
Luisa Garcés-Murillo ◽  
Alexandra Ríos-Orrego ◽  
Jehidys Montiel-Ramos ◽  
Briegel De Las Salas ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Falciparum Malaria ◽  
Therapeutic Efficacy ◽  
Plasma Concentrations ◽  
Plasmodium Falciparum Malaria ◽  
Therapeutic Failure ◽  
World Health ◽  
Blood Levels ◽  
Drug Clinical Trial ◽  
Parasitological Response

Introduction: In Colombia, the published studies for the treatment of uncomplicated Plasmodium falciparum malaria with Artemether-Lumefantrine are scarce. The aim of the study was to evaluate the therapeutic efficacy and safety profile of this combination.Methods: A clinical trial was performed in adults with uncomplicated P. falciparum malaria using the 28-day World Health Organization validated protocol. Patients received supervised antimalarial treatment and the primary efficacy endpoint was the clinical and parasitological response. Safety was assessed through adverse events surveillance and plasmatic levels of antimalarial drugs were measured.Results: 88 patients were included. Adequate clinical and parasitological response rate of 100% on day 28 was achieved in 84 patients, diagnosed by thick blood smear examination. There were four parasitological therapeutic failures (5%) detected by polymerase chain reaction.Discusion: Therapeutic efficacy similar to previous studies was established with a slight increase in therapeutic failure. The serum levels of the antimalarials were adequate and the few cases of therapeutic failure were not related.Conclusion: Treatment of uncomplicated P. falciparum malaria with Artemeter-Lumefantrine was effective and safe in the study population. All patients reached adequate plasma concentrations of the drugs; therapeutic failures were not associated with low blood levels of the drug clinical trial.

scholarly journals A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development (Preprint)

2020 ◽  
Author(s):  
Jian Du ◽  
Xiaoying Li
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Clinical Practice ◽  
Practice Guidelines ◽  
Biomedical Literature ◽  
Knowledge Graph ◽  
Combination Therapies ◽  
Combination Drug ◽  
Automated Knowledge ◽  
Semantic Predications

BACKGROUND Combination therapy plays an important role in the effective treatment of malignant neoplasms and precision medicine. Numerous clinical studies have been carried out to investigate combination drug therapies. Automated knowledge discovery of these combinations and their graphic representation in knowledge graphs will enable pattern recognition and identification of drug combinations used to treat a specific type of cancer, improve drug efficacy and treatment of human disorders. OBJECTIVE This paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical trial reports and clinical practice guidelines. METHODS Based on semantic predications, which consist of a triple structure of subject-predicate-object (SPO), we proposed an automated algorithm to discover knowledge of combination drug therapies using the following rules: 1) two or more semantic predications (S<sub>1</sub>-P-O and S<sub>i</sub>-P-O, i = 2, 3…) can be extracted from one conclusive claim (sentence) in the abstract of a given publication, and 2) these predications have an identical predicate (that closely relates to human disease treatment, eg, “treat”) and object (eg, disease name) but different subjects (eg, drug names). A customized knowledge graph organizes and visualizes these combinations, improving the traditional semantic triples. After automatic filtering of broad concepts such as “pharmacologic actions” and generic disease names, a set of combination drug therapies were identified and characterized through manual interpretation. RESULTS We retrieved 22,263 clinical trial reports and 31 clinical practice guidelines from PubMed abstracts by searching “antineoplastic agents” for drug restriction (published between Jan 2009 and Oct 2019). There were 15,603 conclusive claims locally parsed using the search terms “conclusion*” and “conclude*” ready for semantic predications extraction by SemRep, and 325 candidate groups of semantic predications about combined medications were automatically discovered within 316 conclusive claims. Based on manual analysis, we determined that 255/316 claims (78.46%) were accurately identified as describing combination therapies and adopted these to construct the customized knowledge graph. We also identified two categories (and 4 subcategories) to characterize the inaccurate results: limitations of SemRep and limitations of proposal. We further learned the predominant patterns of drug combinations based on mechanism of action for new combined medication studies and discovered 4 obvious markers (“combin*,” “coadministration,” “co-administered,” and “regimen”) to identify potential combination therapies to enable development of a machine learning algorithm. CONCLUSIONS Semantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. A machine learning approach is warranted to take full advantage of the identified markers and other contextual features.

scholarly journals The appearance and increase in the quantity and proportion of the clinical research coordinator's service fee in drug clinical trial research fund and its impact on trial quality

2020 ◽  
Author(s):  
Liran Chen ◽  
Zhimin Chen ◽  
Huafang Chen
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Research ◽  
Structural Changes ◽  
Continuous Increase ◽  
Clinical Trial Research ◽  
Clinical Research Coordinator ◽  
Research Coordinator ◽  
Drug Clinical Trial

Abstract Objective: The changes of absolute value and relative value of clinical research coordinator service fee and its influence on the quality of drug clinical trial were analyzed.Methods: This study compared the amount and structural changes of drug clinical trial costs in before 3 years and after 3 years of self-examination and inspection initiated by the China Food and Drug Administration, identified the increase number and composition of each individual cost of a clinical trial research funds which including clinical research coordinator service fee, investigator labor fee, subjects examination fee, subjects traffic subsidy, documents management fee, drug management fee, etc.Result: The most significant appearance and increase in volume and proportion were the clinical research coordinator service fee. From the initial few to the global multicenter tumor drug clinical trials RMB31,624 or 34.92% of the proportion and domestic multicenter tumor drug clinical trials RMB16,500,accounted for 33.74%.Discussion: It has become common for more money to be spent on clinical trials to be accompanied by improved quality, but the occurrence and continuous increase of clinical research coordinator service fee were divided into two aspects, On the one hand, the quality of clinical trials was promoted by the large amount of low-skill trivial work undertaken by clinical research coordinator; on the other hand, the quality of clinical trials was undermined by the fact that clinical research coordinator did too much treatment evaluation work that should have been done by the investigator.

A global phase III multicenter, randomized, double-arm, open label trial of ASP-1929 photoimmunotherapy versus physician’s choice standard of care for the treatment of patients with locoregional, recurrent head and neck squamous cell carcinoma (rHNSCC).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. TPS6094-TPS6094 ◽  
Author(s):  
Merrill A. Biel ◽  
Ann M. Gillenwater ◽  
David M. Cognetti ◽  
Jennifer Maria Johnson ◽  
Athanassios Argiris ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Controlled Trial ◽  
Standard Of Care ◽  
Light Treatment ◽  
Phase Iii ◽  
Tumor Cell Death ◽  
Open Label ◽  
Combination Drug ◽  
Phase 3 ◽  
Curative Therapy

TPS6094 Background: rHNSCC commonly affects local or regional sites and is associated with considerable morbidity and mortality. Outcomes of these patients remain poor with limited curative treatment options and low response rates. New modalities that are targeted, minimally invasive, and provide improved tumor response and control while having limited systemic side effects are needed. Photoimmunotherapy (PIT) is a new cancer-targeted platform technology. It is a combination drug and device treatment that utilizes monoclonal antibodies conjugated to a dye (IRDye 700DX) that is photoactivated using nonthermal red light to induce rapid and selective tumor cell death. The objective of this phase 3 study is to evaluate the efficacy and safety of ASP-1929 (EGFR-directed antibody cetuximab-IR700 conjugate) PIT treatment as a monotherapy in patients with locoregional rHNSCC. Methods: A global, multicenter phase 3, randomized, double-arm, open-label, controlled trial of ASP-1929 PIT vs physician’s choice standard of care (SOC) for the treatment of locoregional, rHNSCC in patients who have failed or progressed on or after at least two lines of therapy, of which at least one line must be systemic therapy, is currently underway. Primary endpoints of the study are PFS and OS and the key secondary endpoint is ORR. Key inclusion criteria include: disease not amenable to curative therapy; tumor(s) accessible for PIT light treatment and measurable by CT or MRI; male or female ≥ 18 yrs old with life expectancy > 6 months; ECOG score of 0 to 1. Key exclusion criteria include: history of ≥ Grade 3 cetuximab infusion reaction; distant metastatic disease; tumors invading a major blood vessel unless embolized. The study will include ~275 subjects in a 2:1 randomization (ASP-1929 PIT: Physician’s choice SOC). The physician’s choice SOC arm includes cetuximab, methotrexate, or docetaxel. Tumor(s) are illuminated with 690 nm PIT light treatment 24 hrs following completion of ASP-1929 infusion (640 mg/m²). Clinical trial sites will be in the USA, EU and Asia. Clinical trial information: NCT03769506.

scholarly journals A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development

10.2196/18323 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e18323
Author(s):  
Jian Du ◽  
Xiaoying Li
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Clinical Practice ◽  
Practice Guidelines ◽  
Biomedical Literature ◽  
Knowledge Graph ◽  
Combination Therapies ◽  
Combination Drug ◽  
Automated Knowledge ◽  
Semantic Predications

Background Combination therapy plays an important role in the effective treatment of malignant neoplasms and precision medicine. Numerous clinical studies have been carried out to investigate combination drug therapies. Automated knowledge discovery of these combinations and their graphic representation in knowledge graphs will enable pattern recognition and identification of drug combinations used to treat a specific type of cancer, improve drug efficacy and treatment of human disorders. Objective This paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical trial reports and clinical practice guidelines. Methods Based on semantic predications, which consist of a triple structure of subject-predicate-object (SPO), we proposed an automated algorithm to discover knowledge of combination drug therapies using the following rules: 1) two or more semantic predications (S1-P-O and Si-P-O, i = 2, 3…) can be extracted from one conclusive claim (sentence) in the abstract of a given publication, and 2) these predications have an identical predicate (that closely relates to human disease treatment, eg, “treat”) and object (eg, disease name) but different subjects (eg, drug names). A customized knowledge graph organizes and visualizes these combinations, improving the traditional semantic triples. After automatic filtering of broad concepts such as “pharmacologic actions” and generic disease names, a set of combination drug therapies were identified and characterized through manual interpretation. Results We retrieved 22,263 clinical trial reports and 31 clinical practice guidelines from PubMed abstracts by searching “antineoplastic agents” for drug restriction (published between Jan 2009 and Oct 2019). There were 15,603 conclusive claims locally parsed using the search terms “conclusion*” and “conclude*” ready for semantic predications extraction by SemRep, and 325 candidate groups of semantic predications about combined medications were automatically discovered within 316 conclusive claims. Based on manual analysis, we determined that 255/316 claims (78.46%) were accurately identified as describing combination therapies and adopted these to construct the customized knowledge graph. We also identified two categories (and 4 subcategories) to characterize the inaccurate results: limitations of SemRep and limitations of proposal. We further learned the predominant patterns of drug combinations based on mechanism of action for new combined medication studies and discovered 4 obvious markers (“combin*,” “coadministration,” “co-administered,” and “regimen”) to identify potential combination therapies to enable development of a machine learning algorithm. Conclusions Semantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. A machine learning approach is warranted to take full advantage of the identified markers and other contextual features.

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