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      Evaluación de la relación exposición-respuesta de cetuximab en pacientes con cáncer colorrectal metastásico y cáncer de cabeza y cuello Translated title: Assessment of exposure-response relationship for cetuximab in patients with metastatic colorectal cancer and head and neck cancer

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          Abstract

          Resumen Objetivo: Evaluar, en condiciones de vida real, la relación entre las concentraciones valle en estado estacionario de cetuximab y el control de la enfermedad, así como buscar la relación entre estas concentraciones y la supervivencia. Además, estudiar si existe una concentración límite que se pueda asociar con la probabilidad de beneficio clínico. Método: Estudio observacional prospectivo llevado a cabo en pacientes con cáncer colorrectal metastásico o cáncer de cabeza y cuello en tratamiento con cetuximab. Se realizó un análisis de regresión de ecuaciones de estimación generalizadas para evaluar la asociación entre la concentración valle en estado estacionario de cetuximab y la respuesta al tratamiento (progresión o beneficio clínico). Mediante modelos de riesgos proporcionales de Cox, se evaluó la asociación entre la mediana de concentraciones valle en estado estacionario de cetuximab en cada paciente o la última medida con la supervivencia global y la supervivencia libre de progresión, en cada una de las patologías. Asimismo, se buscó un punto de corte óptimo a través del área bajo la curva de características operativas del receptor. Resultados: Se analizaron 30 muestras de 16 pacientes. La concentración valle en estado estacionario mediana fue 26,86 mg/l y se encontró una gran variabilidad inter e intraindividual (desviación estándar de 32,4 y 16,9 mg/l, respectivamente). Se observó una asociación positiva entre la concentración valle en estado estacionario y el beneficio clínico (odds ratio 1,24; intervalo de confianza del 95%: 0,95-1,63; p = 0,113), aunque no alcanzó significación estadística debido a la baja potencia. El área bajo la curva de características operativas del receptor de las concentraciones (n = 30) tuvo una moderada capacidad discriminatoria (área bajo la curva de características operativas del receptor 0,710; intervalo de confianza del 95%: 0,49-0,93) y el punto de corte estimado fue de 19,12 mg/l. Sin embargo, no se observó relación entre la supervivencia y las concentraciones valle en estado estacionario en ninguna de las patologías. Conclusiones: No se ha podido confirmar una relación entre exposición a cetuximab y eficacia, a pesar de encontrar una tendencia positiva en el control de la enfermedad con el aumento de la concentración valle en estado estacionario. El nivel de evidencia se vio reducido por la pequeña muestra de pacientes en cada grupo, por lo que se necesitan estudios aleatorizados y controlados, con un número suficiente de pacientes, para evaluar adecuadamente esta relación.

          Translated abstract

          Abstract Objective: There is limited scientific evidence on the cetuximab exposure- response relationship and no concentration threshold has been associated with optimal disease control. The aims were to assess, in a real-life setting, the relationship between steady state cetuximab concentrations (Ctrough, SS) and disease control. Method: A prospective observational study in patients with metastatic colorectal cancer or head and neck cancer treated with cetuximab. Steady state trough concentrations were compared with the results of radiological assessment of response (progression or clinical benefit). Generalized estimating equations analysis was performed. To test the association between steady state concentrations and overall survival and progression-free survival, Cox proportional hazard models were developed. An optimal cut-off point was searched using the area under the receiver operating characteristic curve. Results: A total of 30 steady state cetuximab concentrations from 16 patients were analysed. Median Ctrough, SS was 26.86 mg/L and there was marked inter- and intraindividual variability (standard deviation 32.4 mg/L and 16.9 mg/L, respectively). A positive association was found between cetuximab Ctrough, SS and clinical benefit (odds ratio 1.24, 95% confidence interval: 0.95-1.63, . = 0.113), although without reaching statistical significance. The area under the receiver operating characteristic curve (. = 30) had moderate discrimination power (0.71; 95% confidence interval 0.49‑0.93), and the empirical optimal cutoff point was 19.12 mg/L. However, no association was observed between cetuximab Ctrough, SS and survival in metastatic colorectal cancer or neck cancer patients. Conclusions: We cannot confirm a relationship between cetuximab Ctrough, SS and disease control despite a positive association. This study was conducted with a small sample, which reduces the power analysis. Further controlled randomised studies with a sufficient number of patients are needed.

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          Survival Analysis Part I: Basic concepts and first analyses

          Introduction In many cancer studies, the main outcome under assessment is the time to an event of interest. The generic name for the time is survival time, although it may be applied to the time ‘survived’ from complete remission to relapse or progression as equally as to the time from diagnosis to death. If the event occurred in all individuals, many methods of analysis would be applicable. However, it is usual that at the end of follow-up some of the individuals have not had the event of interest, and thus their true time to event is unknown. Further, survival data are rarely Normally distributed, but are skewed and comprise typically of many early events and relatively few late ones. It is these features of the data that make the special methods called survival analysis necessary. This paper is the first of a series of four articles that aim to introduce and explain the basic concepts of survival analysis. Most survival analyses in cancer journals use some or all of Kaplan–Meier (KM) plots, logrank tests, and Cox (proportional hazards) regression. We will discuss the background to, and interpretation of, each of these methods but also other approaches to analysis that deserve to be used more often. In this first article, we will present the basic concepts of survival analysis, including how to produce and interpret survival curves, and how to quantify and test survival differences between two or more groups of patients. Future papers in the series cover multivariate analysis and the last paper introduces some more advanced concepts in a brief question and answer format. More detailed accounts of these methods can be found in books written specifically about survival analysis, for example, Collett (1994), Parmar and Machin (1995) and Kleinbaum (1996). In addition, individual references for the methods are presented throughout the series. Several introductory texts also describe the basis of survival analysis, for example, Altman (2003) and Piantadosi (1997). TYPES OF ‘EVENT’ IN CANCER STUDIES In many medical studies, time to death is the event of interest. However, in cancer, another important measure is the time between response to treatment and recurrence or relapse-free survival time (also called disease-free survival time). It is important to state what the event is and when the period of observation starts and finishes. For example, we may be interested in relapse in the time period between a confirmed response and the first relapse of cancer. CENSORING MAKES SURVIVAL ANALYSIS DIFFERENT The specific difficulties relating to survival analysis arise largely from the fact that only some individuals have experienced the event and, subsequently, survival times will be unknown for a subset of the study group. This phenomenon is called censoring and it may arise in the following ways: (a) a patient has not (yet) experienced the relevant outcome, such as relapse or death, by the time of the close of the study; (b) a patient is lost to follow-up during the study period; (c) a patient experiences a different event that makes further follow-up impossible. Such censored survival times underestimate the true (but unknown) time to event. Visualising the survival process of an individual as a time-line, their event (assuming it were to occur) is beyond the end of the follow-up period. This situation is often called right censoring. Censoring can also occur if we observe the presence of a state or condition but do not know where it began. For example, consider a study investigating the time to recurrence of a cancer following surgical removal of the primary tumour. If the patients were examined 3 months after surgery to determine recurrence, then those who had a recurrence would have a survival time that was left censored because the actual time of recurrence occurred less than 3 months after surgery. Event time data may also be interval censored, meaning that individuals come in and out of observation. If we consider the previous example and patients are also examined at 6 months, then those who are disease free at 3 months and lost to follow-up between 3 and 6 months are considered interval censored. Most survival data include right censored observations, but methods for interval and left censored data are available (Hosmer and Lemeshow, 1999). In the remainder of this paper, we will consider right censored data only. In general, the feature of censoring means that special methods of analysis are needed, and standard graphical methods of data exploration and presentation, notably scatter diagrams, cannot be used. ILLUSTRATIVE STUDIES Ovarian cancer data This data set relates to 825 patients diagnosed with primary epithelial ovarian carcinoma between January 1990 and December 1999 at the Western General Hospital in Edinburgh. Follow-up data were available up until the end of December 2000, by which time 550 (75.9%) had died (Clark et al, 2001). Figure 1 Figure 1 Converting calendar time in the ovarian cancer study to a survival analysis format. Dashed vertical line is the date of the last follow-up, R=relapse, D=death from ovarian cancer, Do=death from other cause, A=attended last clinic visit (alive), L=loss to follow-up, X=death, □=censored. shows data from 10 patients diagnosed in the early 1990s and illustrates how patient profiles in calendar time are converted to time to event (death) data. Figure 1 (left) shows that four patients had a nonfatal relapse, one was lost to follow-up, and seven patients died (five from ovarian cancer). In the other plot, the data are presented in the format for a survival analysis where all-cause mortality is the event of interest. Each patient's ‘survival’ time has been plotted as the time from diagnosis. It is important to note that because overall mortality is the event of interest, nonfatal relapses are ignored, and those who have not died are considered (right) censored. Figure 1 (right) is specific to the outcome or event of interest. Here, death from any cause, often called overall survival, was the outcome of interest. If we were interested solely in ovarian cancer deaths, then patients 5 and 6 – those who died from nonovarian causes – would be censored. In general, it is good practice to choose an end-point that cannot be misclassified. All-cause mortality is a more robust end-point than a specific cause of death. If we were interested in time to relapse, those who did not have a relapse (fatal or nonfatal) would be censored at either the date of death or the date of last follow-up. Lung cancer clinical trial data These data originate from a phase III clinical trial of 164 patients with surgically resected (non-small cell) lung cancer, randomised between 1979 and 1985 to receive radiotherapy either with or without adjuvant combination platinum-based chemotherapy (Lung Cancer Study Group, 1988; Piantadosi, 1997). For the purposes of this series, we will focus on the time to first relapse (including death from lung cancer). Table 1 Table 1 A sample of times (days) to relapse among patients randomised to receive radiotherapy with or without adjuvant chemotherapy Radiotherapy (n=86) 18, 23a, 25, 27, 28, 30, 36, 45, 55, 56, 57, 57, 57, 59, 62, …,   2252a, 2286a, 2305a, 2318a, 2940a Radiotherapy+CAP (n=78) 9, 22, 35, 53, 76, 81, 94, 97, 103, 114, 115, 126, 147, 154, …,   2220a, 2375, 2566, 2875b, 3067b CAP=cytoxan, doxorubicin and platinum-based chemotherapy. a Lost to follow-up and considered censored. b Relapse-free at time of analysis and considered censored. gives the time of the earliest 15 and latest five relapses for each treatment group, where it can be seen that some patients were alive and relapse-free at the end of the study. The relapse proportions in the radiotherapy and combination arms were 81.4% (70 out of 86) and 69.2% (54 out of 78), respectively. However, these figures are potentially misleading as they ignore the duration spent in remission before these events occurred. SURVIVAL AND HAZARD Survival data are generally described and modelled in terms of two related probabilities, namely survival and hazard. The survival probability (which is also called the survivor function) S(t) is the probability that an individual survives from the time origin (e.g. diagnosis of cancer) to a specified future time t. It is fundamental to a survival analysis because survival probabilities for different values of t provide crucial summary information from time to event data. These values describe directly the survival experience of a study cohort. The hazard is usually denoted by h(t) or λ(t) and is the probability that an individual who is under observation at a time t has an event at that time. Put another way, it represents the instantaneous event rate for an individual who has already survived to time t. Note that, in contrast to the survivor function, which focuses on not having an event, the hazard function focuses on the event occurring. It is of interest because it provides insight into the conditional failure rates and provides a vehicle for specifying a survival model. In summary, the hazard relates to the incident (current) event rate, while survival reflects the cumulative non-occurrence. KAPLAN–MEIER SURVIVAL ESTIMATE The survival probability can be estimated nonparametrically from observed survival times, both censored and uncensored, using the KM (or product-limit) method (Kaplan and Meier, 1958). Suppose that k patients have events in the period of follow-up at distinct times t 1
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            Trough concentrations of infliximab guide dosing for patients with inflammatory bowel disease.

            Infliximab, a tumor necrosis factor antagonist, is effective for treating patients with Crohn's disease (CD) and ulcerative colitis (UC). We aimed to determine whether dosing based on therapeutic drug monitoring increases rate of remission and whether continued concentration-based dosing is superior to clinically based dosing of infliximab for maintaining remission in patients with CD and UC.
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              American Gastroenterological Association Institute Technical Review on the Role of Therapeutic Drug Monitoring in the Management of Inflammatory Bowel Diseases

              Therapeutic drug monitoring (TDM), which involves measurement of drug or active metabolite levels and anti-drug antibodies, is a promising strategy that can be used to optimize inflammatory bowel disease therapeutics. It is based on the premise that there is a relationship between drug exposure and outcomes, and that considerable inter-individual variability exists in how patients metabolize the drug (pharmacokinetics) and the magnitude and duration of response to therapy (pharmacodynamics). Therefore, the American Gastroenterological Association has prioritized clinical guidelines on the role of TDM in the management of inflammatory bowel disease. To inform these clinical guidelines, this technical review was developed in accordance with the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) framework for interventional and prognostic studies, and focused on the application of TDM for biologic therapy, specifically anti-tumor necrosis factor-α agents, and for thiopurines. Focused questions address the benefits and risks of a strategy of reactive TDM (in patients with active inflammatory bowel disease) to guide treatment changes compared with empiric treatment changes, and the benefits and risks of a strategy of routine proactive TDM (during routine clinical care in patients with quiescent disease) compared with no routine TDM. Additionally, the review addresses the benefits and risks of routine measurement of thiopurine methyltransferase enzyme activity or genotype before starting thiopurine therapy compared with empiric weight-based dosing and explores the performance of different trough drug concentrations for anti-tumor necrosis factor agents and thiopurines to inform clinical decision making when applying TDM in a reactive setting. Due to a paucity of data, this review does not address the role of TDM for more recently approved biologic agents, such as vedolizumab or ustekinumab.
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                Author and article information

                Journal
                fh
                Farmacia Hospitalaria
                Farm Hosp.
                Grupo Aula Médica (Toledo, Toledo, Spain )
                1130-6343
                2171-8695
                February 2022
                : 46
                : 1
                : 21-26
                Affiliations
                [6] Madrid orgnameHospital Universitario Severo Ochoa orgdiv1Servicio de Oncología Médica España
                [3] Tenerife orgnameHospital Universitario de Canarias orgdiv1Instituto de Investigación Sanitaria de Canarias orgdiv2Fundación Canaria España
                [2] Madrid orgnameCIBERESP orgdiv1Instituto de Investigación Sanitaria Puerta de Hierro-Segovia de Arana orgdiv2Unidad de Bioestadística España
                [1] Madrid orgnameHospital Universitario Severo Ochoa, orgdiv1Servicio de Farmacia España
                [4] Tenerife orgnameHospital Universitario de Canarias orgdiv1Unidad de Investigación España
                [5] Madrid Madrid orgnameUniversidad Alfonso X el Sabio Spain
                Article
                S1130-63432022000100021 S1130-6343(22)04600100021
                10.7399/fh.11790
                76e6779c-25c2-4ca9-8656-7f3fc73b0ba0

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 22 October 2021
                : 06 July 2021
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 30, Pages: 6
                Product

                SciELO Spain

                Categories
                Originales

                Monitorización de fármacos,Resultados del tratamiento,Relación dosis-respuesta,Cetuximab,Cáncer de cabeza y cuello,Cáncer colorrectal,Farmacocinética,Head and neck neoplasms,Colorectal neoplasms,Pharmacokinetics,Drug monitoring,Treatment outcome,Dose-response relationship

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