RESUMEN
BACKGROUND: Targeting the DNA damage repair (DDR) pathways is an attractive strategy for boosting cancer immunotherapy. Ceralasertib (AZD6738) is an oral kinase inhibitor of ataxia telangiectasia and Rad3 related protein, which is a master regulator of DDR. We conducted a phase II trial of ceralasertib plus durvalumab in patients with previously treated advanced gastric cancer (AGC) to demonstrate the safety, tolerability, and clinical activity of the combination. METHODS: This phase II, open-label, single-center, non-randomized study was designed to evaluate the efficacy and safety of ceralasertib in combination with durvalumab in patients with AGC. The study drug regimen was ceralasertib (240 mg two times a day) days 15-28 in a 28-day cycle in combination with durvalumab (1500 mg) at day 1 every 4 weeks. The primary end point was overall response rate (ORR) by Response Evaluation Criteria in Solid Tumors (V.1.1). Exploratory biomarker analysis was performed using fresh tumor biopsies in all enrolled patients. RESULTS: Among 31 patients, the ORR, disease control rate, median progression-free survival (PFS), and overall survival were 22.6% (95% CI 9.6% to 41.1%), 58.1% (95% CI 39.1% to 75.5%), 3.0 (95% CI 2.1 to 3.9) months, and 6.7 (95% CI 3.8 to 9.6) months, respectively. Common adverse events were manageable with dose modification. A subgroup of patients with a loss of ataxia telangiectasia mutated (ATM) expression and/or high proportion of mutational signature attributable to homologous repair deficiency (sig. HRD) demonstrated a significantly longer PFS than those with intact ATM and low sig. HRD (5.60 vs 1.65 months; HR 0.13, 95% CI 0.045 to 0.39; long-rank p<0.001). During the study treatment, upregulation of the innate immune response by cytosolic DNA, activation of intratumoral lymphocytes, and expansion of circulating tumor-reactive CD8 +T cell clones were identified in responders. Enrichment of the tumor vasculature signature was associated with treatment resistance. CONCLUSIONS: Ceralasertib plus durvalumab has promising antitumor activity, with durable responses in patients with refractory AGC. Thus, a biomarker-driven trial is required. TRIAL REGISTRATION: NCT03780608.
Asunto(s)
Anticuerpos Monoclonales , Protocolos de Quimioterapia Combinada Antineoplásica , Inhibidores de Proteínas Quinasas , Neoplasias Gástricas , Anticuerpos Monoclonales/administración & dosificación , Anticuerpos Monoclonales/uso terapéutico , Antineoplásicos/administración & dosificación , Antineoplásicos/uso terapéutico , Antineoplásicos Inmunológicos/administración & dosificación , Antineoplásicos Inmunológicos/uso terapéutico , Protocolos de Quimioterapia Combinada Antineoplásica/administración & dosificación , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapéutico , Proteínas de la Ataxia Telangiectasia Mutada/antagonistas & inhibidores , Proteínas de la Ataxia Telangiectasia Mutada/genética , Humanos , Indoles/administración & dosificación , Indoles/uso terapéutico , Morfolinas/administración & dosificación , Morfolinas/uso terapéutico , Inhibidores de Proteínas Quinasas/administración & dosificación , Inhibidores de Proteínas Quinasas/uso terapéutico , Pirimidinas/administración & dosificación , Pirimidinas/uso terapéutico , Neoplasias Gástricas/tratamiento farmacológico , Neoplasias Gástricas/genética , Neoplasias Gástricas/patología , Sulfonamidas/administración & dosificación , Sulfonamidas/uso terapéutico , Sulfóxidos/administración & dosificación , Sulfóxidos/uso terapéuticoRESUMEN
Intraoperative targeting of the analgesic effect still lacks an optimal solution. Opioids are currently the main drug used to achieve antinociception, and although underdosing can lead to an increased stress response, overdose can also lead to undesirable adverse effects. To better understand how to achieve the optimal analgesic effect of opioids, we studied the influence of remifentanil on the pupillary reflex dilation (PRD) and its relationship with the reflex movement response to a standardized noxious stimulus. The main objective was to generate population pharmacodynamic models relating remifentanil predicted concentrations to movement and to pupillary dilation during general anesthesia. A total of 78 patients undergoing gynecological surgery under general anesthesia were recruited for the study. PRD and movement response to a tetanic stimulus were measured multiple times before and after surgery. We used nonlinear mixed effects modeling to generate a population pharmacodynamic model to describe both the time profiles of PRD and movement responses to noxious stimulation. Our model demonstrated that movement and PRD are equally depressed by remifentanil. Using the developed model, we changed the intensity of stimulation and simulated remifentanil predicted concentrations maximizing the probability of absence of movement response. An estimated effect site concentration of 2 ng/ml of remifentanil was found to inhibit movement to a tetanic stimulation with a probability of 81%.
Asunto(s)
Analgésicos Opioides , Reflejo Pupilar , Analgésicos Opioides/farmacología , Anestesia General , Dilatación , Humanos , Reflejo Pupilar/fisiología , RemifentaniloRESUMEN
PURPOSE: This study reports the safety, tolerability, MTD, recommended phase II dose (RP2D), pharmacokinetic/pharmacodynamic profile, and preliminary antitumor activity of ceralasertib combined with carboplatin in patients with advanced solid tumors. It also examined exploratory predictive and pharmacodynamic biomarkers. PATIENTS AND METHODS: Eligible patients (n = 36) received a fixed dose of carboplatin (AUC5) with escalating doses of ceralasertib (20 mg twice daily to 60 mg once daily) in 21-day cycles. Sequential and concurrent combination dosing schedules were assessed. RESULTS: Two ceralasertib MTD dose schedules, 20 mg twice daily on days 4-13 and 40 mg once daily on days 1-2, were tolerated with carboplatin AUC5; the latter was declared the RP2D. The most common treatment-emergent adverse events (Common Terminology Criteria for Adverse Events grade ≥3) were anemia (39%), thrombocytopenia (36%), and neutropenia (25%). Dose-limiting toxicities of grade 4 thrombocytopenia (n = 2; including one grade 4 platelet count decreased) and a combination of grade 4 thrombocytopenia and grade 3 neutropenia occurred in 3 patients. Ceralasertib was quickly absorbed (tmax â¼1 hour), with a terminal plasma half-life of 8-11 hours. Upregulation of pRAD50, indicative of ataxia telangiectasia mutated (ATM) activation, was observed in tumor biopsies during ceralasertib treatment. Two patients with absent or low ATM or SLFN11 protein expression achieved confirmed RECIST v1.1 partial responses. Eighteen of 34 (53%) response-evaluable patients had RECIST v1.1 stable disease. CONCLUSIONS: The RP2D for ceralasertib plus carboplatin was established as ceralasertib 40 mg once daily on days 1-2 administered with carboplatin AUC5 every 3 weeks, with pharmacokinetic and pharmacodynamic studies confirming pharmacodynamic modulation and preliminary evidence of antitumor activity observed.
Asunto(s)
Neoplasias , Neutropenia , Trombocitopenia , Protocolos de Quimioterapia Combinada Antineoplásica/efectos adversos , Proteínas de la Ataxia Telangiectasia Mutada/genética , Carboplatino , Humanos , Indoles , Dosis Máxima Tolerada , Morfolinas , Neoplasias/etiología , Neutropenia/inducido químicamente , Proteínas Nucleares , Inhibidores de Proteínas Quinasas/efectos adversos , Pirimidinas , Sulfonamidas , Sulfóxidos , Trombocitopenia/inducido químicamenteRESUMEN
PURPOSE: Ceralasertib is a potent and selective oral inhibitor of the serine/threonine protein kinase ataxia telangiectasia and Rad3-related (ATR) protein. PATIENTS AND METHODS: Eligible patients with solid tumors, enriched for melanoma, received ceralasertib in combination with a fixed dose of paclitaxel (80 mg/m2 on D1, D8, D15) in 28-day cycles. The dose of ceralasertib was escalated to reach an MTD in a rolling 6 design. The starting dose of ceralasertib was 40 mg QD. Fifty-seven patients (33 patients with melanoma who failed prior PD1/L1 treatment) were enrolled in 7 dose cohorts ranging from 40 mg QD to 240 mg BD plus weekly paclitaxel. RESULTS: The RP2D was established as ceralasertib 240 mg BD days 1-14 plus paclitaxel 80 mg/m2 on D1, D8, D15 every 28 days. The most common toxicities were neutropenia (n = 39, 68%), anemia (n = 25, 44%), and thrombocytopenia (n = 21, 37%). In the full analysis set of 57 patients, the overall response rate (ORR) was 22.6% (95% CI, 12.5-35.3). In 33 patients with melanoma, resistant to prior anti-PD1 therapy, the ORR was 33.3% (95% CI, 18.0-51.8). In the melanoma subset, the mPFS was 3.6 months (95% CI, 2.0-5.8), the median duration of response was 9.9 months (95% CI, 3.7-23.2), and the mOS was 7.4 months (95% CI, 5.7-11.9). CONCLUSIONS: Ceralasertib in combination with paclitaxel was well tolerated in patients with advanced malignancies and showed evidence of antitumor activity. Durable responses were observed in patients with advanced cutaneous, acral, and mucosal melanoma resistant to anti-PD1/L1 treatment.See related commentary by Ashworth, p. 4667.
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Antineoplásicos Fitogénicos/administración & dosificación , Indoles/administración & dosificación , Morfolinas/administración & dosificación , Neoplasias/tratamiento farmacológico , Paclitaxel/administración & dosificación , Pirimidinas/administración & dosificación , Sulfonamidas/administración & dosificación , Adulto , Anciano , Esquema de Medicación , Combinación de Medicamentos , Femenino , Humanos , Masculino , Melanoma/tratamiento farmacológico , Persona de Mediana Edad , Neoplasias Cutáneas/tratamiento farmacológicoRESUMEN
Many drugs that have been proposed for treatment of coronavirus disease 2019 (COVID-19) are reported to cause cardiac adverse events, including ventricular arrhythmias. In order to properly weigh risks against potential benefits, particularly when decisions must be made quickly, mathematical modeling of both drug disposition and drug action can be useful for predicting patient response and making informed decisions. Here, we explored the potential effects on cardiac electrophysiology of four drugs proposed to treat COVID-19: lopinavir, ritonavir, chloroquine, and azithromycin, as well as combination therapy involving these drugs. Our study combined simulations of pharmacokinetics (PKs) with quantitative systems pharmacology (QSP) modeling of ventricular myocytes to predict potential cardiac adverse events caused by these treatments. Simulation results predicted that drug combinations can lead to greater cellular action potential prolongation, analogous to QT prolongation, compared with drugs given in isolation. The combination effect can result from both PK and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that women with pre-existing heart disease are especially susceptible to drug-induced arrhythmias, compared with diseased men or healthy individuals of either sex. Statistical analysis of population simulations revealed the molecular factors that make certain women with heart failure especially susceptible to arrhythmias. Overall, the results illustrate how PK and QSP modeling may be combined to more precisely predict cardiac risks of COVID-19 therapies.
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Antivirales/administración & dosificación , Antivirales/efectos adversos , Arritmias Cardíacas/inducido químicamente , Tratamiento Farmacológico de COVID-19 , Modelos Teóricos , Terapias en Investigación/métodos , Potenciales de Acción/efectos de los fármacos , Potenciales de Acción/fisiología , Arritmias Cardíacas/diagnóstico , Arritmias Cardíacas/fisiopatología , Azitromicina/administración & dosificación , Azitromicina/efectos adversos , COVID-19/metabolismo , Cloroquina/administración & dosificación , Cloroquina/efectos adversos , Combinación de Medicamentos , Interacciones Farmacológicas/fisiología , Quimioterapia Combinada , Femenino , Humanos , Lopinavir/administración & dosificación , Lopinavir/efectos adversos , Masculino , Miocitos Cardíacos/efectos de los fármacos , Miocitos Cardíacos/metabolismo , Factores de Riesgo , Ritonavir/administración & dosificación , Ritonavir/efectos adversosRESUMEN
The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug-disease models more versatile and realistic.
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Desarrollo de Medicamentos/métodos , Descubrimiento de Drogas/métodos , Modelos Biológicos , Humanos , Procesos EstocásticosRESUMEN
Identification of optimal schedules for combination drug administration relies on accurately estimating the correct pharmacokinetics, pharmacodynamics, and drug interaction effects. Misspecification of pharmacokinetics can lead to wrongly predicted timing or order of treatments, leading to schedules recommended on the basis of incorrect assumptions about absorption and elimination of a drug and its effect on tumor growth. Here, we developed a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data. The software can be used to compare prespecified schedules on the basis of the number of resistant cells where drug interactions and pharmacokinetic curves can be estimated from user-provided data or models. We applied our approach to publicly available in vitro data of treatment with different tyrosine kinase inhibitors of BT-20 triple-negative breast cancer cells and of treatment with erlotinib of PC-9 non-small cell lung cancer cells. Our approach is publicly available in the form of an R package called ACESO (https://github.com/Michorlab/aceso) and can be used to investigate optimum dosing for any combination treatment. SIGNIFICANCE: These findings introduce a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data.
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Carcinoma de Pulmón de Células no Pequeñas/tratamiento farmacológico , Clorhidrato de Erlotinib/farmacocinética , Neoplasias Pulmonares/tratamiento farmacológico , Inhibidores de Proteínas Quinasas/farmacocinética , Programas Informáticos , Neoplasias de la Mama Triple Negativas/tratamiento farmacológico , Antineoplásicos/administración & dosificación , Antineoplásicos/farmacocinética , Carcinoma de Pulmón de Células no Pequeñas/metabolismo , Muerte Celular , Esquema de Medicación , Sinergismo Farmacológico , Clorhidrato de Erlotinib/administración & dosificación , Femenino , Humanos , Neoplasias Pulmonares/metabolismo , Inhibidores de Proteínas Quinasas/administración & dosificación , Quinolinas/administración & dosificación , Quinolinas/farmacocinética , Tiazoles/administración & dosificación , Tiazoles/farmacocinética , Neoplasias de la Mama Triple Negativas/metabolismoRESUMEN
Many drugs that have been proposed for treatment of COVID-19 are reported to cause cardiac adverse events, including ventricular arrhythmias. In order to properly weigh risks against potential benefits, particularly when decisions must be made quickly, mathematical modeling of both drug disposition and drug action can be useful for predicting patient response and making informed decisions. Here we explored the potential effects on cardiac electrophysiology of 4 drugs proposed to treat COVID-19: lopinavir, ritonavir, chloroquine, and azithromycin, as well as combination therapy involving these drugs. Our study combined simulations of pharmacokinetics (PK) with quantitative systems pharmacology (QSP) modeling of ventricular myocytes to predict potential cardiac adverse events caused by these treatments. Simulation results predicted that drug combinations can lead to greater cellular action potential prolongation, analogous to QT prolongation, compared with drugs given in isolation. The combination effect can result from both pharmacokinetic and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that females with pre-existing heart disease are especially susceptible to drug-induced arrhythmias, compared males with disease or healthy individuals of either sex. Overall, the results illustrate how PK and QSP modeling may be combined to more precisely predict cardiac risks of COVID-19 therapies.
RESUMEN
Advanced melanoma remains a disease with poor prognosis. Several serologic markers have been investigated to help monitoring and prognostication, but to date only lactate dehydrogenase (LDH) has been validated as a standard prognostic factor biomarker for this disease by the American Joint Committee on Cancer. In this work, we built a semi-mechanistic model to explore the relationship between the time course of several circulating biomarkers and overall or progression free survival in advanced melanoma patients treated with adjuvant high-dose interferon-[Formula: see text]. Additionally, due to the adverse interferon tolerability, a semi-mechanistic model describing the side effects of the treatment in the absolute neutrophil counts is proposed in order to simultaneously analyze the benefits and toxic effects of this treatment. The results of our analysis suggest that the relative change from baseline of LDH was the most significant predictor of the overall survival of the patients. Unfortunately, there was no significant difference in the proportion of patients with elevated serum biomarkers between the patients who recurred and those who remained free of disease. Still, we believe that the modelling framework presented in this work of circulating biomarkers and adverse effects could constitute an additional strategy for disease monitoring in advance melanoma patients.
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Biomarcadores de Tumor/sangre , Melanoma , Modelos Biológicos , Neoplasias Cutáneas , Adulto , Anciano , Quimioterapia Adyuvante , Supervivencia sin Enfermedad , Femenino , Humanos , Masculino , Melanoma/sangre , Melanoma/tratamiento farmacológico , Melanoma/mortalidad , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Neoplasias Cutáneas/sangre , Neoplasias Cutáneas/tratamiento farmacológico , Neoplasias Cutáneas/mortalidad , Tasa de SupervivenciaRESUMEN
Numerous problems encountered in computational biology can be formulated as optimization problems. In this context, optimization of drug release characteristics or dosing schedules for anticancer agents has become a prominent area not only for the development of new drugs, but also for established drugs. However, in complex systems, optimization of drug exposure is not a trivial task and cannot be efficiently addressed through trial-error simulation exercises. Finding a solution to those problems is a challenging task which requires more advanced strategies like optimal control theory. In this work, we perform an optimal control analysis on a previously developed computational model for the testosterone effects of triptorelin in prostate cancer patients with the goal of finding optimal drug-release characteristics. We demonstrate how numerical control optimization of non-linear models can be used to find better therapeutic approaches in order to improve the final outcome of the patients.
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Antineoplásicos/administración & dosificación , Neoplasias de la Próstata/tratamiento farmacológico , Antineoplásicos/farmacocinética , Antineoplásicos Hormonales/administración & dosificación , Antineoplásicos Hormonales/farmacocinética , Biología Computacional , Simulación por Computador , Sistemas de Liberación de Medicamentos/estadística & datos numéricos , Humanos , Masculino , Modelos Biológicos , Neoplasias Hormono-Dependientes/sangre , Neoplasias Hormono-Dependientes/tratamiento farmacológico , Dinámicas no Lineales , Orquiectomía/métodos , Neoplasias de la Próstata/sangre , Testosterona/sangre , Pamoato de Triptorelina/administración & dosificaciónRESUMEN
MOTIVATION: The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets. RESULTS: In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s.
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Algoritmos , Enfermedades Inflamatorias del Intestino/metabolismo , Metaloproteinasas de la Matriz/metabolismo , Modelos Biológicos , Simulación por Computador , Humanos , Enfermedades Inflamatorias del Intestino/tratamiento farmacológico , Interferón gamma/antagonistas & inhibidores , Interferón gamma/metabolismo , Interleucina-17/antagonistas & inhibidores , Interleucina-17/metabolismo , Terapia Molecular Dirigida/métodos , Factor de Necrosis Tumoral alfa/antagonistas & inhibidores , Factor de Necrosis Tumoral alfa/metabolismoRESUMEN
Motivation: Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets. Results: In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets. Availability and Implementation: The source code is available at https://github.com/SPIDDOR/SPIDDOR . Contact: itzirurzun@alumni.unav.es , itroconiz@unav.es. Supplementary information: Supplementary data are available at Bioinformatics online.
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Descubrimiento de Drogas , Biología de Sistemas/métodos , Enfermedad , Modelos Teóricos , Transducción de Señal , Programas InformáticosRESUMEN
Drug development in Systemic Lupus Erythematosus (SLE) has been hindered by poor translation from successful preclinical experiments to clinical efficacy. This lack of success has been attributed to the high heterogeneity of SLE patients and to the lack of understanding of disease physiopathology. Modelling approaches could be useful for supporting the identification of targets, biomarkers and patient subpopulations with differential response to drugs. However, the use of traditional quantitative models based on differential equations is not justifiable in a sparse data situation. Boolean networks models are less demanding on the required data to be implemented and can provide insights into the dynamics of biological networks. This methodology allows the integration of all the available knowledge into a single framework to evaluate the behavior of the system under different conditions and test hypotheses about unknown aspects of the disease. In this proof-of-concept study, we explored the potential of a systems pharmacology model based on Boolean networks to support drug development in SLE. We focused the analysis on the antigen presentation by the antigen presenting cells (APC) to the T-cells to evaluate the scope of this methodology in a medium size network before full implementation of the whole SLE pathway. The heterogeneity of SLE patients was replicated using this methodology simulating subjects with distinct pathway alterations. A perturbation analysis of the network coupled with clustering analysis showed potential to identify drug targets, optimal combinatorial regimens and subpopulations of responders and non-responders to drug treatment. We propose this approach as a first step towards the development of more quantitative platforms to address the current challenges in drug development for complex diseases.