Sergey Aksenov

Siberian State Medical University

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Michael A. Savageau

University of California, Davis

Gabriel Helmlinger

Quantitative BioSciences

Nidal Al‐Huniti

Regeneron (United States)

Peter J. Mohr

Roche (Switzerland)

G. Soff

TU Dresden

Ekaterina Artemova

National Research University Higher School of Economics

Kirill Peskov

Sechenov University

Murat Apishev

Lomonosov Moscow State University

Oleg Serikov

National Research University Higher School of Economics

Diansong Zhou

AstraZeneca (United States)

Cooperative Institutions

AstraZeneca (United States)

AstraZeneca (Sweden)

AstraZeneca (United Kingdom)

Novartis (United States)

Novartis (Switzerland)

University of Florida

AstraZeneca (Brazil)

Columbus Oncology and Hematology Associates

National Research Tomsk State University

Amgen (United States)

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Implementation of the Combined--Nonlinear Condensation Transformation

arXiv (Cornell University) (2002)

Sergey Aksenov Michael A. Savageau Ulrich D. Jentschura Jens Becher G. Soff

We discuss several applications of the recently proposed combined nonlinear-condensation transformation (CNCT) for the evaluation of slowly convergent, nonalternating series. These include certain statistical distributions which are of importance in linguistics, statistical-mechanics theory, and biophysics (statistical analysis of DNA sequences). We also discuss applications of the transformation in experimental mathematics, and we briefly expand on further applications in theoretical physics. Finally, we discuss a related Mathematica program for the computation of Lerch's transcendent.

Statistical Mechanics

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Citations (18)

Mathematical Model of the SOS Response Regulation of an Excision Repair Deficient Mutant ofEscherichia coliafter Ultraviolet Light Irradiation

Journal of Theoretical Biology (1997)

Sergey Aksenov E. A. Krasavin А. А. Литвин

Repressor lexA

SOS response

Pyrimidine dimer

Ultraviolet light

10.1006/jtbi.1996.0353

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Citations (17)

A Joint Approach to Compound Splitting and Idiomatic Compound Detection

arXiv (Cornell University) (2020)

И В Кротова Sergey Aksenov Ekaterina Artemova

Compound

Proper noun

Splitter

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Population Pharmacokinetics of Bruton Tyrosine Kinase Inhibitor, Acalabrutinib, in Healthy Volunteers and Patients with B-Cell Malignancies

Blood (2017)

Helena Edlund Marilee Andrew Feng Jin Priti Patel Eric Masson

NONMEM

Follicular lymphoma

10.1182/blood.v130.suppl_1.4997.4997

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Citations (1)

Statistical inference and modeling with the S distribution

arXiv (Cornell University) (2001)

Sergey Aksenov Michael A. Savageau

We consider the problem of statistical inference for the S distribution and introduce new minimum distance estimators for the four parameters of the S distribution using Kolmogorov-Smirnov, Cramer-von Mises and related distance metrics. Approximate goodness-of-fit and confidence intervals for parameters are calculated using bootstrap methods. We discuss further how the S distribution can be used to solve various problems of statistical modeling associated with parameter inference, including goodness-of-fit tests, Monte Carlo simulations and modeling trends in the distributions.

Goodness of fit

Statistical Inference

Fiducial inference

Sampling distribution

Anderson–Darling test

10.48550/arxiv.physics/0112046

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Citations (1)

Population Pharmacokinetics of Inhaled Tobramycin Powder in Cystic Fibrosis Patients

CPT Pharmacometrics & Systems Pharmacology (2014)

Lillian Ting Sergey Aksenov SG Bhansali R. Ramakrishna Patricia A. Tang

Tobramycin powder for inhalation (TOBI Podhaler or TIP) is approved for the treatment of Pseudomonas aeruginosa airway infection in patients with cystic fibrosis (CF). A population pharmacokinetic model for tobramycin inhalation powder (TIP) in CF patients was developed to characterize the effect of covariates including body mass index (BMI) and lung function (forced expiratory volume in 1 s as percent of the predicted value (FEV1% predicted) at baseline) on the serum exposure parameters.A two-compartment model with first-order elimination and first-order absorption was developed. Across a range of baseline demographic values in the study population, the predicted mean values for the maximum (Cmax) and trough (Ctrough) plasma concentrations at steady state were at least 7.5 and 5-fold lower, respectively, than the recommended thresholds for tobramycin toxicity (12 µg/ml for Cmax and 2 µg/ml for Ctrough). This model adequately described the tobramycin serum concentration-time course in CF patients following inhalation of TIP. The results indicate that no BMI- or FEV1-based dose adjustment is needed for use of TIP in CF patients.

10.1038/psp.2013.76

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Citations (13)

Teaching a Massive Open Online Course on Natural Language Processing

arXiv (Cornell University) (2021)

Ekaterina Artemova Murat Apishev Veronika Sarkisyan Sergey Aksenov Denis Kirjanov

This paper presents a new Massive Open Online Course on Natural Language Processing, targeted at non-English speaking students. The course lasts 12 weeks; every week consists of lectures, practical sessions, and quiz assignments. Three weeks out of 12 are followed by Kaggle-style coding assignments. Our course intends to serve multiple purposes: (i) familiarize students with the core concepts and methods in NLP, such as language modeling or word or sentence representations, (ii) show that recent advances, including pre-trained Transformer-based models, are built upon these concepts; (iii) introduce architectures for most demanded real-life applications, (iv) develop practical skills to process texts in multiple languages. The course was prepared and recorded during 2020, launched by the end of the year, and in early 2021 has received positive feedback.

Course (navigation)

Online course

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Pharmacometric Modeling of Naloxegol Efficacy and Safety: Impact on Dose and Label

Clinical Pharmacology & Therapeutics (2017)

Nidal Al‐Huniti Diansong Zhou Hongmei Xu Sergey Aksenov KH Bui

Naloxegol is a peripherally acting μ‐opioid receptor antagonist that was developed for the treatment of opioid‐induced constipation. Modeling and simulation of naloxegol efficacy and tolerability informed selection of doses for phase III studies and provided comprehensive dosage recommendations for the naloxegol US package insert.

Tolerability

10.1002/cpt.719

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Citations (10)

A systems biology dynamical model of mammalian G₁cell cycle progression

Molecular Systems Biology (2007)

Thomas Haberichter Britta Mädge Renee A. Christopher Naohisa Yoshioka Anjali Dhiman

Cyclin A

E2F

Cyclin D

Cyclin E

Cyclin A2

Cyclin B

10.1038/msb4100126

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Citations (64)

Longitudinal tumor size and NLR as predictive factors of individual survival compared to their baseline values in patients with non-small cell lung cancer treated with durvalumab.

Journal of Clinical Oncology (2019)

Kirill Zhudenkov S. V. Gavrilov Kirill Peskov Gabriel Helmlinger Sergey Aksenov

e20047 Background: Our ability to accurately predict survival of patients with non-small cell lung cancer (NSCLC) while on treatment is limited. Prognostic markers such as stage and tumor size are well established, while neutrophil-to-lymphocytes ratio (NLR) and other hemogram measurements have recently been studied. Gain in prognostic accuracy of these markers when measured longitudinally has not been established. Methods: There were 679 NSCLC patients (Stage 3 or 4, ECOG PS 0 or 1) from clinical studies of durvalumab 10 mg/kg every two weeks (NCT02087423 and NCT01693562). We developed three models of overall survival (OS) all with ECOG as covariate: a Cox proportional hazards model with baseline tumor sum-of-longest-diameters (SLD) and NLR as covariates (COX); a joint model of OS and longitudinal SLD and baseline NLR (JM SLD); and a joint model of OS and longitudinal SLD and NLR (JM SLD&NLR). We compared prognostic accuracy of these markers measured longitudinally vs. at baseline, using predicted probability of OS at 12 months after start of durvalumab as a prognostic score. We evaluated predictive performance of the models using area under the receiver-operating characteristic curve (ROC AUC) describing trade-off between true and false positives (i.e., survival past 12 months). The AUCs were calculated for patients in the dataset using longitudinal data up to different cut-offs. Results: The AUC for all patients starting durvalumab using baseline ECOG, SLD and NLR was 0.73, while it decreased to 0.64 for patients surviving to 6 months, compared to 0.50 for noninformative models. The AUC using longitudinal information for SLD and NLR was larger the more longitudinal data was used for prediction and was 0.81 using 6 months’ worth of data. Conclusions: Using longitudinal information for SLD and NLR increased individual predictive performance of these markers compared to only baseline information in NSCLC patients. [Table: see text]

Durvalumab

10.1200/jco.2019.37.15_suppl.e20047

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Citations (2)