NEW ORIGINAL DRUG ROSEOFUNGIN-AS, OINTMENT 2%

2021 ◽  
pp. 55-64
Author(s):  
А.К. САДАНОВ ◽  
В.Э. БЕРЕЗИН ◽  
И.Р. КУЛМАГАМБЕТОВ ◽  
Л.П. ТРЕНОЖНИКОВА ◽  
А.С. БАЛГИМБАЕВА

В статье приводятся сведения о разработке нового отечественного противогрибкового препарата «Розеофунгин-АС, мазь 2%» для наружного применения на основе оригинального природного полиенового антибиотика розеофунгина. Приводятся данные о продуценте антибиотика, процессе его биосинтеза и получения, его физико-химических свойствах и химической структуре, рассматриваются его антифунгальные и антивирусные свойства, механизм его действия, а также основные этапы разработки противогрибкового препарата - доклинические и I, II и III фазы клинических исследований. This paper provides the information on the development of new domestic antifungal drug Roseofungin-AS, ointment 2% for external use based on the original natural polyene antibiotic roseofungin. Data on the antibiotic producer, the process of its biosynthesis and production, its physicochemical properties and chemical structure are presented, its antifungal and antiviral properties, the mechanism of action as well as the main stages of the antifungal drug development including preclinical and phase I, II, III clinical trials are discussed.

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Guillaume Beinse ◽  
Virgile Tellier ◽  
Valentin Charvet ◽  
Eric Deutsch ◽  
Isabelle Borget ◽  
...  

PURPOSE Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a machine learning algorithm (RESOLVED2) to predict drug development outcome, which could support early go/no-go decisions after P1CTs by better selection of drugs suitable for further development. METHODS PubMed abstracts of P1CTs reporting on ANAs were used together with pharmacologic data from the DrugBank5.0 database to model time to US Food and Drug Administration (FDA) approval (FDA approval-free survival) since the first P1CT publication. The RESOLVED2 model was trained with machine learning methods. Its performance was evaluated on an independent test set with weighted concordance index (IPCW). RESULTS We identified 462 ANAs from PubMed that matched with DrugBank5.0 (P1CT publication dates 1972 to 2017). Among 1,411 variables, 28 were used by RESOLVED2 to model the FDA approval-free survival, with an IPCW of 0.89 on the independent test set. RESOLVED2 outperformed a model that was based on efficacy/toxicity (IPCW, 0.69). In the test set at 6 years of follow-up, 73% (95% CI, 49% to 86%) of drugs predicted to be approved were approved, whereas 92% (95% CI, 87% to 98%) of drugs predicted to be nonapproved were still not approved (log-rank P < .001). A predicted approved drug was 16 times more likely to be approved than a predicted nonapproved drug (hazard ratio, 16.4; 95% CI, 8.40 to 32.2). CONCLUSION As soon as P1CT completion, RESOLVED2 can predict accurately the time to FDA approval. We provide the proof of concept that drug development outcome can be predicted by machine learning strategies.


2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 9080-9080
Author(s):  
D. Wang ◽  
E. Heath ◽  
A. Powell ◽  
T. Chaperon ◽  
F. LaGrone ◽  
...  

9080 Phase I oncology clinical trials are critical in the oncology drug development process. To protect human subjects, every phase 1 protocol must be approved by an institutional review board (IRB) to assure safety before patient accrual. As the volume and complexity of phase 1 trials have increased, the amount of time spent on IRB protocol reviews have also increased for various reasons. Objectives: 1) Determine the average time spent on protocol approval by IRB at KCI/WSU; 2) Identify potential issues raised by IRB resulting in approval delays; 3) Identify the redundancies for which “standard language” implementation could facilitate future IRB applications thereby expediting approval. Methods: 96 Phase 1 research IRB applications at KCI/WSU between 8/1/2005 and 10/31/2006 were reviewed. These applications were stratified based on submission (new protocol versus amendment) and IRB approval (tabled, provisional or approved) status. Concerns frequently brought up by the IRB were identified. Results: The average and median time spent from initial submission to final approval of all 96 applications were 41.4 days and 43 days, respectively. Forty eight of 96 applications (50%) were provisionally approved from the initial review. Average and median time of obtaining final approval were 52.5 days and 52 days. Nine of 96 (9.4%) protocols were tabled with their average approval 83 days. The most common concerns raised by IRB were risks/benefit issues. These concerns were an even greater approval barrier when protocols involved specialized technologies of molecular therapeutics or complicated study designs. Regulatory policy changes issued by oversight organizations also required “real-time” updates into protocols and consent form amendments. Areas of “standard language” for future IRB applications are being compiled and will be discussed upon presentation. Conclusion: Phase 1 clinical trials are essential to anti-cancer drug development. The complicated ethical issues and science warrant an ongoing constructive collaboration of both parties. Identification of commonalities that delay IRB approval will lead to more expeditious IRB approval not only at our institution, but could also benefit other institutions. No significant financial relationships to disclose.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5656-5656
Author(s):  
Muhammad Asad Fraz ◽  
Muhammad Junaid Tariq ◽  
Muhammad Usman ◽  
Nadia Carenina Nunes Cavalcante Parr ◽  
Awais Ijaz ◽  
...  

Abstract Introduction Immunotherapy using monoclonal antibodies (mAbs) have been gaining significance in the treatment of multiple myeloma (MM). These include naked antibodies, checkpoint inhibitors (CPIs), novel bispecific mAbs targeting two epitopes and antibody-drug conjugates (ADCs) having a mAb conjugated to a cytotoxic drug. This review aims to summarize phase I and I/II clinical trials using mABs for the treatment of MM. Methods A comprehensive literature search using data from PubMed, Embase, AdisInsight and Clinicaltrials.gov was performed for identification of early phase (I and I/II) trials of mAbs in MM treatment (January 2008 to December 2017). Studies involving mAbs including targeting antibodies, ADCs, CPIs and bispecific mAbs were included, without considering the geo-location, age, sex or specific eligibility criteria. Drugs already approved by FDA were excluded. Results Total of 2537 phase I and phase I/II studies were identified. After screening by two reviewers and categorization by their mechanism of action, 74 clinical trials (CTs) that involved mAbs as monotherapy or in combination with other chemotherapeutic drugs for the treatment of newly diagnosed MM (NDMM) and relapsed/refractory MM (RRMM). 41 CTs are active, completed or discontinued (Table 1) and 33 CTs are recruiting, approved for recruitment or planned. Most explored mechanism of action in these trials was mAb therapy directed against CD38, IL-6, huCD40, PD-L1 and PD-1. Isatuximab (Anti-CD38) has shown objective response rate (ORR) of >50% in combination with lenalidomide (R) or pomalidomide (P) plus dexamethasone (d) in ongoing phase I trials NCT01749969 (n=57) and NCT02283775 (n=89) respectively. According to Vij et al. (2016) and Mikhael et al. (2018), 54% ORR (n=31) and 62% ORR (n=28) was shown by combination of isatuximab with Rd and Pd in 57 and 45 evaluable RRMM patients, respectively. In Vij et al. (2016) study, stringent complete response (sCR) in 2 (3%) patients, very good partial response (VGPR) in 13 (23%) and partial response (PR) in 16 (28%) patients was observed. In Mikhael et al. (2018) study, sCR in 1 (2%) patient, CR in 1 (2%), VGPR in 10 (21%) and PR in 16 (34%) patients was observed. In comparison, Martin et al. (2014) mentioned ORR of only 24% with isatuximab monotherapy in 34 RRMM patients. Grade (G) ≥3 pneumonia (n=4) was the most common high-grade adverse events (AEs) being reported (Table 2). Siltuximab (Anti-IL-6) has shown clinical efficacy in combination with bortezomib (V) + d and RVd in phase I and I/II CTs. Shah et al. (2016) and Suzuki et al. (2015) found ORR to be 90.9% and 67% in 11 (NDMM) and 9 (RRMM) patients when siltuximab was given combined with RVd and Vd, respectively. Clinical benefit response (CBR) i.e. ≥ minimal response (MR) was 100% with siltuximab + RVd in NDMM patients. In comparison, siltuximab monotherapy in 13 RRMM patients yielded an ORR of 15% (2 CR) as reported by Kurzrock et al. (2012). G≥3 neutropenia (n=9), G≥3 thrombocytopenia (n=6) and G≥3 lymphopenia (n=8) were most common reported high-grade AEs. Checkpoint inhibitors including pembrolizumab (anti-PD-1) and pidilizumab (anti-PD-L1) are being investigated in RRMM treatment. According to Otero et al. (2017) and Ribrag et al. (2017), 50% ORR was obtained with pembrolizumab combined with Rd compared to 0% with monotherapy, respectively. However, combination therapy was associated with G≥3 neutropenia (n=17), thrombocytopenia (n=9) and anemia (n=6) while no high-grade AEs were observed with monotherapy. Antibody-Drug conjugates including lorvotuzumab mertansine and indatuximab ravtansine have been investigated in CTs for MM treatment. Lorvotuzumab mertansine has shown clinical efficacy in combination with Rd in a phase I trial (NCT00991562). Berdeja et al. (2012) reported an ORR of 59% (1 sCR, 1 CR, 8 VGPR, 9 PR) in 32 RRMM patients. In a phase I/II trial (NCT01638936) of indatuximab ravtansine combined with either Rd or Pd, Kelly et al. (2016) showed ORR of 77% with Rd (n=43) including at least 1 CR and 4 VGPR and 79% with Pd (n=14) including 4 VGPR in total 57 RRMM patients. Conclusion Combination regimens including monoclonal antibodies, CPIs and ADCs have shown clinically significant response in RRMM and NDMM patients. The mAbs caused hematological and nonhematological AEs like cytopenias and infections which needs to be monitored closely. Disclosures No relevant conflicts of interest to declare.


2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Dongmei Li ◽  
Xiaodong She ◽  
Richard Calderone

ABSTRACT Our review summarizes and compares the temporal development (eras) of antifungal drug discovery as well as antibacterial ventures. The innovation gap that occurred in antibacterial discovery from 1960 to 2000 was likely due to tailoring of existing compounds to have better activity than predecessors. Antifungal discovery also faced innovation gaps. The semi-synthetic antibiotic era was followed closely by the resistance era and the heightened need for new compounds and targets. With the immense contribution of comparative genomics, antifungal targets became part of the discovery focus. These targets by definition are absolutely required to be fungal- or even lineage (clade) specific. Importantly, targets need to be essential for growth and/or have important roles in disease and pathogenesis. Two types of antifungals are discussed that are mostly in the FDA phase I–III clinical trials. New antifungals are either modified to increase bioavailability and stability for instance, or are new compounds that inhibit new targets. One of the important developments in incentivizing new antifungal discovery has been the prolific number of publications of global and country-specific incidence. International efforts that champion global antimicrobial drug discovery are discussed. Still, interventions are needed. The current pipeline of antifungals and alternatives to antifungals are discussed including vaccines.


2019 ◽  
Vol 64 (4) ◽  
pp. 509-519 ◽  
Author(s):  
Daiane F. Dalla Lana ◽  
Ânderson R. Carvalho ◽  
William Lopes ◽  
Marilene H. Vainstein ◽  
Luciano S. P. Guimarães ◽  
...  

2014 ◽  
Vol 20 (22) ◽  
pp. 5663-5671 ◽  
Author(s):  
Victor Moreno García ◽  
David Olmos ◽  
Carlos Gomez-Roca ◽  
Philippe A. Cassier ◽  
Rafael Morales-Barrera ◽  
...  

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. 3084-3084 ◽  
Author(s):  
P. A. Cassier ◽  
V. Moreno Garcia ◽  
C. Gomez-Roca ◽  
D. Olmos ◽  
R. Morales ◽  
...  

2020 ◽  
Vol 6 (3) ◽  
pp. 142
Author(s):  
Kyle McEvoy ◽  
Tyler G. Normile ◽  
Maurizio Del Poeta

Fungal infections are becoming more prevalent and problematic due to the continual rise of immune deficient patients as well as the progressive development of drug resistance towards currently available antifungal drugs. There has been a significant increase in the development of antifungal compounds with a similar mechanism of action of current drugs. In contrast, there has been very little progress in developing compounds inhibiting totally new fungal targets or/and fungal pathways. This review focuses on novel compounds recently discovered to target the fungal sphingolipids and their metabolizing enzymes.


Sign in / Sign up

Export Citation Format

Share Document