M Dezfulian

Harvard University

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Co-Authors

John G. Bartlett

Ontario Institute for Cancer Research

Stephen J. Elledge

Brigham and Women's Hospital

William L. Crosby

University of Windsor

Thomas Pranzatelli

National Institutes of Health

John A. Chiorini

National Institute of Dental and Craniofacial Research

V. R. Dowell

Centers for Disease Control and Prevention

Senthil K. Muthuswamy

Beth Israel Deaconess Medical Center

Qingda Meng

Zhangqiu City People's Hospital

Christopher J. Lessard

Oklahoma Medical Research Foundation

Luis Rueda

University of Windsor

Cooperative Institutions

National Institutes of Health

Harvard University

National Institute of Dental and Craniofacial Research

Johns Hopkins University

Howard Hughes Medical Institute

Brigham and Women's Hospital

Johns Hopkins Medicine

Beth Israel Deaconess Medical Center

University of Windsor

Virginia Commonwealth University

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Efficacy of Moxalactam in an Animal Model of Subcutaneous Abscesses: Penetration into Infected Sites and in Vivo Activity Against Bacteroides fragilis

Clinical Infectious Diseases (1982)

John G. Bartlett Keith A. Joiner M Dezfulian George J. R. Marien

Moxalactam was compared with alternative antimicrobial agents for in vivo activity against Bacteroides fragilis. Mice were challenged with one of 15 strains and evaluated by quantitative bacteriologic analysis of abscess contents after five days of treatment. Optimal results were achieved with clindamycin, moxalactam, and cefoxitin. The mean decrease in bacteria with these three drugs was significantly greater than with chloramphenicol, cefotaxime, carbenicillin, cephalothin, ampicillin, and cefoperazone. The penetration of antimicrobial agents into subcutaneous abscesses was examined. The mean peak level in sterile abscesses of moxalactam was 27% of the mean peak serum level, a result indicating relatively good penetration compared with other beta-lactam antimicrobial agents. Stability of moxalactam to the beta-lactamase of B. fragilis was demonstrated in vivo by comparing levels of biologically active drug in sterile and infected abscesses and in vitro by comparing levels of antibiotic incubated in sterile exudate and exudate infected with B. fragilis. Moxalactam compares favorably with clindamycin and cefoxitin in activity against B. fragilis.

Moxalactam

Bacteroides fragilis

Carbenicillin

Cefotetan

Latamoxef

Cefazolin

Cefmetazole

Fusobacterium

Ceftizoxime

10.1093/clinids/4.supplement_3.s664

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Citations (3)

Aerobic growth and toxigenicity ofClostridium botulinum types A and B

Folia Microbiologica (1999)

M Dezfulian

Clostridium botulinum

Isolation

10.1007/bf02816236

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Citations (6)

A Computational Domain-Based Feature Grouping Approach for Prediction of Stability of SCF Ligases

Lecture notes in computer science (2015)

Mina Maleki M Dezfulian Luis Rueda

Feature (linguistics)

Feature vector

10.1007/978-3-319-16483-0_61

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Citations (1)

Characterization of SCF Ubiquitin-Ligase Subunits in Arabidopsis

M Dezfulian

Ubiquitin-Protein Ligases

Source

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Citations (0)

Evaluation of SAR for Amphotericin B Derivatives by Artificial Neural Network

Tropical Journal of Pharmaceutical Research (2007)

Soroush Sardari M Dezfulian

Purpose: This study was designed to investigate the role of several descriptive structure-activity features in the antifungal drug, amphotericin B and analyze them by artificial neural networks. Method: Artificial neural networks (ANN) based on the back-propagation algorithm were applied to a structure-activity relationship (SAR) study for 17 amphotericin B derivatives with antifungal and membrane directed activity. A series of modified ANN architectures was made and the best result provided the ANN model for prediction of antifungal activity using the structural and biologic property descriptors. Results: The best architecture, in terms of cycles of calculation was 12-15-2. Among the most important factors were biological descriptors that correlated best with the model produced by ANN. Among the chemical and structural descriptors, positive charge on Y substitution was found to be the most important, followed by lack of availability of free carboxyl and parachor. Conclusion: This model is found to be useful to elucidate the structural requirements for the antifungal activity and can be applied in the design and activity prediction of the new amphotericin B derivatives. Keywords: Amphotericin B, SAR, Artificial Neural Network. > Tropical Journal of Pharmaceutical Research Vol. 4 (2) 2005: pp. 517-521

Antifungal drugs

10.4314/tjpr.v4i2.14628

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Citations (2)

Kinetics study of immunological response to Clostridium botulinum toxin

Journal of Clinical Microbiology (1987)

M Dezfulian R A Bitar John G. Bartlett

A double-antibody enzyme-linked immunosorbent assay (ELISA) for detection of humoral antibody to type A botulinal toxin was developed. This assay was used to study the kinetics of antibody response of a volunteer to botulinal toxoid. The circulating type A antitoxin was first detected by the ELISA 2 weeks after the first booster injection of the toxoid. The antibody titer stayed level until the second booster at 12 weeks. The titer then continued to rise throughout the remaining study period. The neutralizing antibody to type A toxin was detected by mouse assay 15 weeks after detection of antitoxin by the ELISA.

Antitoxin

Toxoid

Clostridium botulinum

Antibody titer

10.1128/jcm.25.7.1336-1337.1987

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Citations (7)

Enzyme immunoassay for rapid detection of botulinum toxin and direct identification of Clostridium botulinum colonies.

M Dezfulian Bartlett Jg

Clostridium botulinum

Botulism

Source

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Citations (0)

Physiological characteristics ofClostridium bifermentans selectively isolated from California desert tortoise

Folia Microbiologica (1994)

M Dezfulian Jordan Quintana D. Solleymani David J. Morafka

Tiamulin

Ticarcillin

Mezlocillin

10.1007/bf02814070

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Citations (8)

Cheminformatics in Anti-Infective Agents Discovery

Mini-Reviews in Medicinal Chemistry (2007)

Soroush Sardari M Dezfulian

The existing chemical data such as those created by high throughput screening (HTS), structure-activity relationship (SAR) studies are converted into information as a result of storage and registration. Accessibility, manipulation, and data mining of such information make up the knowledge for drug development. Cheminformatics, exploiting the combination of chemical structural knowledge, biological screening, and data mining approaches is used to guide drug discovery and development and would assist by integrating complex series of rational selection of designed compounds with drug-like properties, building smarter focused libraries. This paper presents cheminformatics approaches and tools for designing and data mining of chemical databases and information. Many examples of success in lead identification and optimization in the area of anti-infective therapy have been discussed. Keywords: Cheminformatics, database, descriptor modeling, anti-infective agent discovery

Cheminformatics

Identification

10.2174/138955707779802633

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Citations (9)

Computational analysis of the stability of SCF ligases employing domain information

Mina Maleki Luis Rueda M Dezfulian William L. Crosby

Because of the unequivocally fundamental role of SCF ubiquitin ligase in many biological functions within a living cell including regulating DNA repair, cell cycle progression, and inflammation, we have analyzed the role of domain interactions in determining particular types of protein-protein interactions (PPIs) that are known or predicted to occur involving subunit components of the SCF-ligase complex. We focus on the prediction and analysis of obligate and non-obligate SCF-ligase complexes by using sequence domains from the Pfam database. After extracting different types of feature vectors, the prediction is performed via a support vector machine (SVM). The numerical results demonstrate that most of the interactions of SCF-ligase complexes are mediated by at least one domain. Moreover, domain-domain interactions dominate in obligate complexes whereas non-obligate complexes exhibit more domain-peptide chain interactions. Also, the computational results show that the best prediction accuracy of 80.46% is achieved using the combination of feature vectors of domain-domain type, domain-peptide chain type and no-domain interactions.

Obligate

10.1145/2649387.2660806

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Citations (2)