Rationale and Technique for Single and Multiple Drug Combinations in Long-term Intrathecal Infusions

Pain Practice ◽  
2001 ◽  
Vol 1 (1) ◽  
pp. 68-80 ◽  
Author(s):  
Leland Lou ◽  
Mauricio Orbegozo ◽  
Casey L. King
Keyword(s):  
Drug Combinations ◽  
Multiple Drug

Rationale and Technique for Single and Multiple Drug Combinations in Long‐term Intrathecal Infusions

Pain Practice ◽  
2001 ◽  
Vol 1 (1) ◽  
pp. 68-80
Author(s):  
Leland Lou ◽  
Mauricio Orbegozo ◽  
Casey L. King
Keyword(s):  
Drug Combinations ◽  
Multiple Drug

Clinical impact of myositis-specific autoantibodies on long-term prognosis of juvenile idiopathic inflammatory myopathies: multicentre study

Rheumatology ◽  
2021 ◽  
Author(s):  
Yuichi Yamasaki ◽  
Norimoto Kobayashi ◽  
Shinji Akioka ◽  
Kazuko Yamazaki ◽  
Shunichiro Takezaki ◽  
...  
Keyword(s):  
Matrix Protein ◽  
Multiple Drug ◽  
Idiopathic Inflammatory Myopathies ◽  
Inflammatory Myopathies ◽  
Gamma Subunits ◽  
Specific Autoantibody ◽  
Long Term Prognosis ◽  
Using Data ◽  
Drug Free

Abstract Objectives This study aimed to investigate the clinical characteristics, treatment and prognosis of juvenile idiopathic inflammatory myopathies (JIIM) in Japan for each myositis-specific autoantibody (MSA) profile. Methods A multicentre, retrospective study was conducted using data of patients with JIIM at nine paediatric rheumatology centres in Japan. Patients with MSA profiles, determined by immunoprecipitation using stored serum from the active stage, were included. Results MSA were detected in 85 of 96 cases eligible for the analyses. Over 90% of the patients in this study had one of the following three MSA types: anti-melanoma differentiation-associated protein 5 (MDA5) (n = 31), anti-transcriptional intermediary factor 1 alpha and/or gamma subunits (TIF1γ) (n = 25) and anti-nuclear matrix protein 2 (NXP2) (n = 25) antibodies. Gottron papules and periungual capillary abnormalities were the most common signs of every MSA group in the initial phase. The presence of interstitial lung disease (ILD) was the highest risk factor for patients with anti-MDA5 antibodies. Most patients were administered multiple drug therapies: glucocorticoids and MTX were administered to patients with anti-TIF1γ or anti-NXP2 antibodies. Half of the patients with anti-MDA5 antibodies received more than three medications including i.v. CYC, especially patients with ILD. Patients with anti-MDA5 antibodies were more likely to achieve drug-free remission (29 vs 21%) and less likely to relapse (26 vs 44%) than others. Conclusion Anti-MDA5 antibodies are the most common MSA type in Japan, and patients with this antibody are characterized by ILD at onset, multiple medications including i.v. CYC, drug-free remission, and a lower frequency of relapse. New therapeutic strategies are required for other MSA types.

scholarly journals Tofacitinib: An Option for Acute Severe Ulcerative Colitis?

2021 ◽  
pp. 1-3
Author(s):  
Sara Santos ◽  
Verónica Gamelas ◽  
Rita Saraiva ◽  
Guilherme Simões ◽  
Joana Saiote ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Clinical Improvement ◽  
Rescue Therapy ◽  
New Drugs ◽  
Multiple Drug ◽  
Disease Course ◽  
Efficacy And Safety ◽  
Severe Ulcerative Colitis ◽  
Rapid Absorption

Tofacitinib has emerged as a new option for ulcerative colitis. Its rapid absorption, metabolism, and clinical improvement make it an interesting option for rescue therapy in acute severe ulcerative colitis (ASUC), a situation with limited therapeutic options in patients with a long-term disease course and multiple drug failure. The management of ASUC in this setting becomes challenging, underlying the need for new drugs and data on their efficacy and safety. We describe 2 cases of acute episodes in which tofacitinib was used as a rescue therapy.

scholarly journals The IDentif.AI 2.0 Pandemic Readiness Platform: Rapid Prioritization of Optimized COVID-19 Combination Therapy Regimens

2021 ◽  
Author(s):  
Agata Blasiak ◽  
Anh TL Truong ◽  
Alexandria Remus ◽  
Lissa Hooi ◽  
Shirley Gek Kheng Seah ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Drug Efficacy ◽  
Drug Combinations ◽  
Multiple Drug ◽  
Original Strain ◽  
Live Virus ◽  
Drug Synergy ◽  
Positive Treatment ◽  
Strong Candidate ◽  
Combination Regimens

Objectives: We aimed to harness IDentif.AI 2.0, a clinically actionable AI platform to rapidly pinpoint and prioritize optimal combination therapy regimens against COVID-19. Methods: A pool of starting candidate therapies was developed in collaboration with a community of infectious disease clinicians and included EIDD-1931 (metabolite of EIDD-2801), baricitinib, ebselen, selinexor, masitinib, nafamostat mesylate, telaprevir (VX-950), SN-38 (metabolite of irinotecan), imatinib mesylate, remdesivir, lopinavir, and ritonavir. Following the initial drug pool assessment, a focused, 6-drug pool was interrogated at 3 dosing levels per drug representing nearly 10,000 possible combination regimens. IDentif.AI 2.0 paired prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus (propagated, original strain and B.1.351 variant) and Vero E6 assay with a quadratic optimization workflow. Results: Within 3 weeks, IDentif.AI 2.0 realized a list of combination regimens, ranked by efficacy, for clinical go/no-go regimen recommendations. IDentif.AI 2.0 revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived. Conclusions: IDentif.AI 2.0 rapidly revealed promising drug combinations for a clinical translation. It pinpointed dose-dependent drug synergy behavior to play a role in trial design and realizing positive treatment outcomes. IDentif.AI 2.0 represents an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

scholarly journals Intravenous Infusion Tests Have Limited Utility for Selecting Long-term Drug Therapy in Patients with Chronic Pain

2009 ◽  
Vol 111 (2) ◽  
pp. 416-431 ◽  
Author(s):  
Steven P. Cohen ◽  
Shruti G. Kapoor ◽  
James P. Rathmell
Keyword(s):  
Systematic Review ◽  
Drug Therapy ◽  
Intravenous Infusion ◽  
Multiple Drug ◽  
Poor Responders ◽  
Intravenous Lidocaine ◽  
Temporary Relief ◽  
Drug Classes ◽  
Convincing Data

Since the first description in the early 1990s, the scope of intravenous infusions tests has expanded to encompass multiple drug classes and indications. Purported advantages of these tests include elucidating mechanisms of pain, providing temporary relief of symptoms, and usefulness as prognostic tools in guiding drug therapy. In an attempt to discern the value of these tests, the authors conducted a systematic review to explore the rationale and evidence behind the following intravenous infusion tests: lidocaine, ketamine, opioid, and phentolamine. The studies evaluating all intravenous infusion tests were characterized by lack of standardization, wide variations in outcome measures, and methodological flaws. The strongest evidence found was for the intravenous lidocaine test, with the phentolamine test characterized by the least convincing data. Whereas intravenous opioid infusions are the most conceptually appealing test, their greatest utility may be in predicting poor responders to sustained-release formulations.

scholarly journals Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

mBio ◽  
2019 ◽  
Vol 10 (6) ◽  
Author(s):  
Shuyi Ma ◽  
Suraj Jaipalli ◽  
Jonah Larkins-Ford ◽  
Jenny Lohmiller ◽  
Bree B. Aldridge ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Computational Model ◽  
Drug Combinations ◽  
Broth Culture ◽  
Multiple Drug ◽  
Content Type ◽  
Drug Synergy ◽  
Drug Regimens

ABSTRACT The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10−4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens. IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

scholarly journals Synergistic Activity of R207910 Combined with Pyrazinamide against Murine Tuberculosis

2006 ◽  
Vol 51 (3) ◽  
pp. 1011-1015 ◽  
Author(s):  
M. Ibrahim ◽  
K. Andries ◽  
N. Lounis ◽  
A. Chauffour ◽  
C. Truffot-Pernot ◽  
...  
Keyword(s):  
Bactericidal Activity ◽  
Treatment Duration ◽  
Drug Combinations ◽  
Synergistic Activity ◽  
Second Line ◽  
Antituberculous Drugs ◽  
Long Term Experiments ◽  
Culture Positive ◽  
Culture Negative

ABSTRACT In previous studies, the diarylquinoline R207910 (also known as TMC207) was demonstrated to have high bactericidal activity when combined with first- or second-line antituberculous drugs. Here we extend the evaluation of R207910 in the curative model of murine tuberculosis by assessing the activities of one-, two-, and three-drug combinations containing R207910 and isoniazid (INH), rifampin (RIF), pyrazinamide (PZA), or moxifloxacin (MXF) in the setting of a high initial bacillary load (7.2 log10 CFU). Two months of treatment with the combinations R207910-PZA, R207910-PZA-INH, R207910-PZA-RIF, or R207910-PZA-MXF resulted in culture-negative lung homogenates in 70 to 100% of the mice, while mice treated with INH-RIF-PZA (the reference regimen) or RIF-MXF-PZA remained culture positive. Combinations including R207910 but not PZA (e.g., R207910-INH-RIF and R207910-MXF-RIF) were less active than R207910-PZA-containing regimens administered either alone or with the addition of INH, RIF, or MXF. These results reveal a synergistic interaction between R207910 and PZA. Three-drug combinations containing these two drugs and INH, RIF, or MXF have the potential to significantly shorten the treatment duration in patients, provided that these results can be confirmed in long-term experiments including periods of relapse.

scholarly journals Modeling drug combination effects via latent tensor reconstruction

2021 ◽  
Author(s):  
Tianduanyi Wang ◽  
Sandor Szedmak ◽  
Haishan Wang ◽  
Tero Aittokallio ◽  
Tapio Pahikkala ◽  
...  
Keyword(s):  
Drug Combination ◽  
Polynomial Regression ◽  
Predictive Performance ◽  
Predictive Modelling ◽  
Drug Combinations ◽  
Multiple Drug ◽  
Interaction Patterns ◽  
Combination Effects ◽  
Drug Doses ◽  
Cost Efficient

Motivation: Combination therapies have emerged as a powerful treatment modality to overcome drug resistance and improve treatment efficacy. However, the number of possible drug combinations increases very rapidly with the number of individual drugs in consideration which makes the comprehensive experimental screening infeasible in practice. Machine learning models offer time- and cost-efficient means to aid this process by prioritising the most effective drug combinations for further pre-clinical and clinical validation. However, the complexity of the underlying interaction patterns across multiple drug doses and in different cellular contexts poses challenges to the predictive modelling of drug combination effects. Results: We introduce comboLTR, highly time-efficient method for learning complex, nonlinear target functions for describing the responses of therapeutic agent combinations in various doses and cancer cell-contexts. The method is based on a polynomial regression via powerful latent tensor reconstruction. It uses a combination of recommender system-style features indexing the data tensor of response values in different contexts, and chemical and multi-omics features as inputs. We demonstrate that comboLTR outperforms state-of-the-art methods in terms of predictive performance and running time, and produces highly accurate results even in the challenging and practical inference scenario where full dose-response matrices are predicted for completely new drug combinations with no available combination and monotherapy response measurements in any training cell line.

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