scholarly journals The effect of apnea length on vital parameters in apnea of prematurity – Hybrid observations from clinical data and simulation in a mathematical model

2022 ◽  
pp. 105536
Author(s):  
Gabriele Varisco ◽  
Irene Lensen ◽  
Deedee Kommers ◽  
Peter Andriessen ◽  
Peter Bovendeerd ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Clinical Data ◽  
Apnea Of Prematurity ◽  
Vital Parameters

Optimal Interval for Detection of Molecular Relapse after Stop of Tyrosine Kinase Inhibitor (TKI) in Philadelphia Chromosome Positive Acute Lymphoblastic Leukemia (Ph+ALL) Caliculated By Kinetics of BCR-ABL Transcripts

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 21-21
Author(s):  
Koichi Miyamura ◽  
Masahide Osaki ◽  
Tatsunori Goto ◽  
Takanobu Morishita ◽  
Yukiyasu Ozawa
Keyword(s):  
Mathematical Model ◽  
Growth Rate ◽  
Clinical Data ◽  
Kinetic Data ◽  
Doubling Time ◽  
Tumor Burden ◽  
Molecular Response ◽  
Optimal Frequency ◽  
Optimal Interval ◽  
As If

Background In Ph+ALL patients, prophylactic/maintenance administrations of TKI are used after the protocol treatment. Unlike Chronic myelogenous leukemia (CML), after TKI stopped, there is no consensus about how often to monitor to detect molecular relapse. In the current study, from clinical data of 31 patients we tried to determine the optimal frequency of MRD monitor for better prognosis using a mathematical model. Methods Doubling time (DT) and Growth rate (GR) were retrospectively calculated by the increase of BCR-ABL from 99 kinetic data from 31 patients with Ph+ALL at molecular relapse. Measurement of amount BCR-ABL was performed by RQ-PCR. Mimicking CML, we defined BCR-ABL/ABL ratio of 0.1% as "MR3 (Major molecular response)" and undetectable levels of BCR-ABL transcript as "MR5 (Complete molecular response)". In order to investigate the relationship between tumor burden (BCR/ABL) at the time of MRD detection and prognosis, it was divided into 3 groups, BCR/ABL<0.1% (MR3), 0.1%<BCR/ABL<1% (MR2) and BCR/ABL>1% (MR1). This study was approved by the institutional review committee. Results The doubling time was 1.3 days (GR 0.7%/day) to 95.4 days (70.8%/day) with a median of 12.3 days (5.8%). The rate of increase was compared by three groups of tumor burden. The DT among patients in MR1 was shorter than those in MR3 and MR2 (7.7 days vs 15.2 days in median, t-test p<0.01). There were no differences of DT in sex, age, treatments and BCR/ABL mutation. Most patient had multiple kinetic data and the smallest amount of BCR/ABL was used in each patient. Among 8 patients who showed MR3 (MR3pt) and 11 who showed MR2 (MR2pt), 14 are alive at this analysis, while among 10 patients who showed MR1 (MR1pt), only 1 patient is alive.(Log-rank test, p<0.1) (Figure) Median survival time is 882 days, 330 days and 16 days in MR3pt, Mr2pt and MR1pt, respectively. All patients died of progression of the disease. Finding molecular relapse before MR2 may related to better results. Optimal interval of MRD detection We set several hypotheses to determine the optimal frequency to detect early recurrence of leukemia. We estimated that patients with MR2 and MR5 have 1010 and 107 Ph+ cells in body, respectively. We defined "MR2" and "MR5" as "optimal intervention threshold" and "detection threshold". From our clinical data, we tentatively determined that doubling time of leukemia growth is distributed between 1 day (GR 100%/day) and 100days (0.7%). Also, we assumed that single cell has a relapse potency and the GR is constant during observation in each patient. The number of Ph+ cells in a MR5 patient who would potentially relapse might be distributed between 1 cell and 1 x 107 (MR5). "Success" was defined as if molecular relapse is detected between M5 and MR2 and "Failure" was defined as if detected more than MR2. According to the daily clinical practice, the optimal examination interval is tentatively every 7 days, every 14 days, every 28 days, every 56 days, every 84 days, every 6 months, and every year. First, in order to calculate the growth rate that would be successful at 7-day intervals, the rate of 168%/day for 1 cell to increase 107 (MR5) in 7 days was calculated. This rate is faster than the maximum rate of 100%/day in this study, so an interval of 7 days is good for the initial period. Similarly, at 14-day intervals, 68%/day is calculated, and in this case, it requires 32 days to increase 107 (MR5) from 1 cell. As a result, after 32.7 days, 14-day intervals are acceptable. Similarly, the calculated results for 28 days, 56 days, 84 days, 6 months, and 1 year are shown in the Table. MRD can be found before M3 (success) with 28-day intervals after 65 days, 56-day intervals after 131 days, 84-day intervals after 196 days, 6-month intervals after 419 days and 1-year intervals after 852 days. Conclusion Taken together, soon after stop of TKI, more frequent monitoring of MRD than in "treatment free remission" in CML is needed. The interval can be prolonged with the passage of time. In several patients, hematopoietic stem cell transplantation (HSCT) was possible due to early intervention by changing TKI and chemotherapy. After HSCT, TKI combined with rapid reduction of immune-suppressants and donor lymphocyte infusion successfully related with long term survival. (data will be shown in ASH) Thus, the tight monitoring according to the mathematical model is important. The current strategy may be applied to other leukemia in which MRD monitoring by PCR is established. Disclosures Miyamura: Bristol-Myers Squibb Co., Ltd.:Honoraria;Celgene Co., Ltd.:Honoraria;Daiichi-Sankyo Co., Ltd.:Honoraria;Otsuka Co., Ltd.:Honoraria;Pfizer Co., Ltd.:Honoraria;Novartis Co., Ltd.:Honoraria.Goto:Takeda Pharmaceutical Co., Ltd:Honoraria;Novartis Pharma Co., Ltd.:Honoraria.Morishita:Bristol-Myers Squibb Co., Ltd.:Honoraria.Ozawa:Novartis Co., Ltd.:Honoraria.

Mathematical model of antibody targeting: important parameters defined using clinical data

2001 ◽  
Vol 46 (6) ◽  
pp. 1679-1693 ◽  
Author(s):  
A J Green ◽  
C J Johnson ◽  
K L Adamson ◽  
R H J Begent
Keyword(s):  
Mathematical Model ◽  
Clinical Data ◽  
Antibody Targeting

scholarly journals Role of IL-23 neutralization in psoriasis – insights from a mechanistic PK/PD model and meta-analysis of clinical data

2021 ◽  
Author(s):  
Georgi I. Kapitanov
Keyword(s):  
Mathematical Model ◽  
Monoclonal Antibodies ◽  
Patient Outcomes ◽  
Clinical Efficacy ◽  
Clinical Data ◽  
Meta Analysis ◽  
Current Work ◽  
Therapeutic Molecules ◽  
Dosing Regimens

AbstractBlocking of IL-23 has shown a profound effect on patient outcomes in psoriasis. The current IL-23 binding monoclonal antibodies show differences in dosing regimens, pharmacokinetics, affinity for the target, and efficacy outcomes in the clinic. The goal of the current work is to use a mechanistic pharmacokinetics/pharmacodynamics mathematical model to estimate projected free IL-23 neutralization for the different therapeutic molecules and connect it to clinical efficacy outcomes. The meta-analysis indicates a sigmoid-like relationship and suggests that the best current anti-IL23 antibodies are close to saturating the efficacy that can be achieved by this pathway in psoriasis.

scholarly journals Verification of Mathematical Model for Upper Limb Spasticity with Clinical Data

2020 ◽  
Vol 824 ◽  
pp. 012013 ◽  
Author(s):  
J Yee ◽  
C Y Low ◽  
P Ong ◽  
W S Soh ◽  
F A Hanapiah ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Clinical Data ◽  
Upper Limb ◽  
Limb Spasticity

scholarly journals Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Neta Tsur ◽  
Yuri Kogan ◽  
Evgenia Avizov-Khodak ◽  
Désirée Vaeth ◽  
Nils Vogler ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Clinical Data ◽  
Checkpoint Inhibitors ◽  
Mechanistic Model ◽  
Advanced Melanoma ◽  
Tumor Growth Rate ◽  
Model Parameters ◽  
Time To Progression ◽  
Number Of Patients ◽  
The Mathematical Model

Abstract Background At present, immune checkpoint inhibitors, such as pembrolizumab, are widely used in the therapy of advanced non-resectable melanoma, as they induce more durable responses than other available treatments. However, the overall response rate does not exceed 50% and, considering the high costs and low life expectancy of nonresponding patients, there is a need to select potential responders before therapy. Our aim was to develop a new personalization algorithm which could be beneficial in the clinical setting for predicting time to disease progression under pembrolizumab treatment. Methods We developed a simple mathematical model for the interactions of an advanced melanoma tumor with both the immune system and the immunotherapy drug, pembrolizumab. We implemented the model in an algorithm which, in conjunction with clinical pretreatment data, enables prediction of the personal patient response to the drug. To develop the algorithm, we retrospectively collected clinical data of 54 patients with advanced melanoma, who had been treated by pembrolizumab, and correlated personal pretreatment measurements to the mathematical model parameters. Using the algorithm together with the longitudinal tumor burden of each patient, we identified the personal mathematical models, and simulated them to predict the patient’s time to progression. We validated the prediction capacity of the algorithm by the Leave-One-Out cross-validation methodology. Results Among the analyzed clinical parameters, the baseline tumor load, the Breslow tumor thickness, and the status of nodular melanoma were significantly correlated with the activation rate of CD8+ T cells and the net tumor growth rate. Using the measurements of these correlates to personalize the mathematical model, we predicted the time to progression of individual patients (Cohen’s κ = 0.489). Comparison of the predicted and the clinical time to progression in patients progressing during the follow-up period showed moderate accuracy (R2 = 0.505). Conclusions Our results show for the first time that a relatively simple mathematical mechanistic model, implemented in a personalization algorithm, can be personalized by clinical data, evaluated before immunotherapy onset. The algorithm, currently yielding moderately accurate predictions of individual patients’ response to pembrolizumab, can be improved by training on a larger number of patients. Algorithm validation by an independent clinical dataset will enable its use as a tool for treatment personalization.

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