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1. A zero-entropy classical shadow reconstruction of density state operators

Author

Montañez-Barrera, J. A., Beretta, G. P., Michielsen, Kristel, and von Spakovsky, Michael R.

Subjects
Quantum Physics
Abstract

Classical shadow (CS) has opened the door to predicting the characteristics of quantum systems using very few measurements. As quantum systems grow in size, new ways to characterize them are needed to show the quality of their qubits, gates, and how noise affects them. In this work, we explore the capabilities of CS for reconstructing density state operators of sections of quantum devices to make a diagnostic of their qubits quality. We introduce zero-entropy classical shadow (ZECS), a methodology that focuses on reconstructing a positive semidefinite and unit trace density state operator using the CS information. This procedure makes a reliable reconstruction of the density state operator removing partially the errors associated with a limited sampling and quantum device noise. It gives a threshold of the maximum coherent information that qubits on a quantum device have. We test ZECS on ibm_lagos and ibm_brisbane using up to 10,000 shots. We show that with only 6,000 shots, we can make a diagnostic of the properties of groups of 2, 3, and 4 qubits on the 127-qubits ibm_brisbane device. We show two applications of ZECS: as a routing technique and as a detector for non-local noisy correlations. In the routing technique, an optimal set of 20 ibm_brisbane qubits is selected based on the ZECS procedure and used for a quantum optimization application. This method improves the solution quality by 10% and extends the quantum algorithm's lifetime by 33% when compared to the qubits chosen by the best transpilation procedure in Qiskit. Additionally, with the detector of non-local correlations, we identify regions of ibm\_brisbane that are not directly connected but have a strong correlation that maintains in time, suggesting some non-local crosstalk that can come, for example, at the multiplexing readout stage., Comment: 13 pages, 10 figures

Published
2024

2. Towards a universal QAOA protocol: Evidence of a scaling advantage in solving some combinatorial optimization problems

Author

Montanez-Barrera, J. A. and Michielsen, Kristel

Subjects
Quantum Physics, Mathematics - Optimization and Control
Abstract

The quantum approximate optimization algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs). In this algorithm, there are alternating layers consisting of a mixer and a problem Hamiltonian. Each layer $i=0,\ldots,p-1$ is parameterized by $\beta_i$ and $\gamma_i$. How to find these parameters has been an open question with the majority of the research focused on finding them using classical algorithms. In this work, we present evidence that fixed linear ramp schedules constitute a universal set of QAOA parameters, i.e., a set of $\gamma$ and $\beta$ parameters that rapidly approximate the optimal solution, $x^*$, independently of the COP selected, and that the success probability of finding it, $probability(x^*)$, increases with the number of QAOA layers $p$. We simulate linear ramp QAOA protocols (LR-QAOA) involving up to $N_q=42$ qubits and $p = 400$ layers on random instances of 9 different COPs. The results suggest that $probability(x^*) \approx 1/2^{(\eta N_q / p)}$ for a constant $\eta$. For example, when implementing LR-QAOA with $p=42$, the $probability(x^*)$ for 42-qubit Weighted MaxCut problems (W-MaxCut) increases from $2/2^{42}\approx 10^{-13}$ to an average of 0.13. We compare LR-QAOA, simulated annealing (SA), and branch-and-bound (B\&B) finding a scaling improvement in LR-QAOA. We test LR-QAOA on real hardware using IonQ Aria, Quantinuum H2-1, IBM Brisbane, IBM Kyoto, and IBM Osaka, encoding random weighted MaxCut (W-MaxCut) problems from 5 to 109 qubits and $p=3$ to $100$. Even for the largest case, $N_q=109$ qubits and $p=100$, information about the LR-QAOA optimization protocol is present. The circuit involved requires 21200 CNOT gates. These results show that LR-QAOA effectively finds high-quality solutions for a large variety of COPs and suggest a scaling advantage of quantum computation for combinatorial optimization.

Published
2024

3. Transfer learning of optimal QAOA parameters in combinatorial optimization

Author

Montanez-Barrera, J. A., Willsch, Dennis, and Michielsen, Kristel

Subjects
Quantum Physics, Mathematics - Optimization and Control
Abstract

Solving combinatorial optimization problems (COPs) is a promising application of quantum computation, with the Quantum Approximate Optimization Algorithm (QAOA) being one of the most studied quantum algorithms for solving them. However, multiple factors make the parameter search of the QAOA a hard optimization problem. In this work, we study transfer learning (TL), a methodology to reuse pre-trained QAOA parameters of one problem instance into different COP instances. To this end, we select small cases of the traveling salesman problem (TSP), the bin packing problem (BPP), the knapsack problem (KP), the weighted maximum cut (MaxCut) problem, the maximal independent set (MIS) problem, and portfolio optimization (PO), and find optimal $\beta$ and $\gamma$ parameters for $p$ layers. We compare how well the parameters found for one problem adapt to the others. Among the different problems, BPP is the one that produces the best transferable parameters, maintaining the probability of finding the optimal solution above a quadratic speedup for problem sizes up to 42 qubits and p = 10 layers. Using the BPP parameters, we perform experiments on IonQ Harmony and Aria, Rigetti Aspen-M-3, and IBM Brisbane of MIS instances for up to 18 qubits. The results indicate IonQ Aria yields the best overlap with the ideal probability distribution. Additionally, we show that cross-platform TL is possible using the D-Wave Advantage quantum annealer with the parameters found for BPP. We show an improvement in performance compared to the default protocols for MIS with up to 170 qubits. Our results suggest that there are QAOA parameters that generalize well for different COPs and annealing protocols., Comment: 11 pages, 8 figures

Published
2024

5. Improving Performance in Combinatorial Optimization Problems with Inequality Constraints: An Evaluation of the Unbalanced Penalization Method on D-Wave Advantage

Author

Montanez-Barrera, J. A., Heuvel, Pim van den, Willsch, Dennis, and Michielsen, Kristel

Subjects
Quantum Physics, Computer Science - Discrete Mathematics
Abstract

Combinatorial optimization problems are one of the target applications of current quantum technology, mainly because of their industrial relevance, the difficulty of solving large instances of them classically, and their equivalence to Ising Hamiltonians using the quadratic unconstrained binary optimization (QUBO) formulation. Many of these applications have inequality constraints, usually encoded as penalization terms in the QUBO formulation using additional variables known as slack variables. The slack variables have two disadvantages: (i) these variables extend the search space of optimal and suboptimal solutions, and (ii) the variables add extra qubits and connections to the quantum algorithm. Recently, a new method known as unbalanced penalization has been presented to avoid using slack variables. This method offers a trade-off between additional slack variables to ensure that the optimal solution is given by the ground state of the Ising Hamiltonian, and using an unbalanced heuristic function to penalize the region where the inequality constraint is violated with the only certainty that the optimal solution will be in the vicinity of the ground state. This work tests the unbalanced penalization method using real quantum hardware on D-Wave Advantage for the traveling salesman problem (TSP). The results show that the unbalanced penalization method outperforms the solutions found using slack variables and sets a new record for the largest TSP solved with quantum technology., Comment: 8 pages, 7 figures, conference

Published
2023
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6. Decoherence predictions in a superconductive quantum device using the steepest-entropy-ascent quantum thermodynamics framework

Author

Montanez-Barrera, J. A., von Spakovsky, M. R., Damian-Ascencio, C. E., and Cano-Andrade, S.

Subjects
Quantum Physics, Physics - Applied Physics, Physics - Data Analysis, Statistics and Probability
Abstract

The current stage of quantum computing technology, called noisy intermediate-scale quantum (NISQ) technology, is characterized by large errors that prohibit it from being used for real applications. In these devices, decoherence, one of the main sources of error, is generally modeled by Markovian master equations such as the Lindblad master equation. In this work, the decoherence phenomena are addressed from the perspective of the steepest-entropy-ascent quantum thermodynamics (SEAQT) framework in which the noise is in part seen as internal to the system. The framework is as well used to describe changes in the energy associated with environmental interactions. Three scenarios, an inversion recovery experiment, a Ramsey experiment, and a two-qubit entanglement-disentanglement experiment, are used to demonstrate the applicability of this framework, which provides good results relative to the experiments and the Lindblad equation, It does so, however, from a different perspective as to the cause of the decoherence. These experiments are conducted on the IBM superconductive quantum device ibmq_bogota., Comment: 11 pages, 8 figures

Published
2022
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7. Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

Author

Montanez-Barrera, J. A., Barroso-Maldonado, J. M., Bedoya-Santacruz, A. F., and Mota-Babiloni, Adrian

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Physics - Fluid Dynamics
Abstract

Accurate pressure drop estimation in forced boiling phenomena is important during the thermal analysis and the geometric design of cryogenic heat exchangers. However, current methods to predict the pressure drop have one of two problems: lack of accuracy or generalization to different situations. In this work, we present the correlated-informed neural networks (CoINN), a new paradigm in applying the artificial neural network (ANN) technique combined with a successful pressure drop correlation as a mapping tool to predict the pressure drop of zeotropic mixtures in micro-channels. The proposed approach is inspired by Transfer Learning, highly used in deep learning problems with reduced datasets. Our method improves the ANN performance by transferring the knowledge of the Sun & Mishima correlation for the pressure drop to the ANN. The correlation having physical and phenomenological implications for the pressure drop in micro-channels considerably improves the performance and generalization capabilities of the ANN. The final architecture consists of three inputs: the mixture vapor quality, the micro-channel inner diameter, and the available pressure drop correlation. The results show the benefits gained using the correlated-informed approach predicting experimental data used for training and a posterior test with a mean relative error (mre) of 6%, lower than the Sun & Mishima correlation of 13%. Additionally, this approach can be extended to other mixtures and experimental settings, a missing feature in other approaches for mapping correlations using ANNs for heat transfer applications.

Published
2022
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8. Method for Generating Randomly Perturbed Density Operators Subject to Different Sets of Constraints

Author

Montanez-Barrera, J. A., Holladay, R. T., Beretta, G. P., and von Spakovsky, Michael R.

Subjects
Quantum Physics
Abstract

This paper presents a general method for producing randomly perturbed density operators subject to different sets of constraints. The perturbed density operators are a specified "distance" away from the state described by the original density operator. This approach is applied to a bipartite system of qubits and used to examine the sensitivity of various entanglement measures on the perturbation magnitude. The constraint sets used include constant energy, constant entropy, and both constant energy and entropy. The method is then applied to produce perturbed random quantum states that correspond with those obtained experimentally for Bell states on the IBM quantum device ibmq_manila. The results show that the methodology can be used to simulate the outcome of real quantum devices where noise, which is important both in theory and simulation, is present., Comment: 14 pages, 16 figures

Published
2021
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9. Testing Swampland Conjectures with Machine Learning

Author

Bizet, Nana Cabo, Damian, Cesar, Loaiza-Brito, Oscar, Peña, Damián Kaloni Mayorga, and Montañez-Barrera, J. A.

Subjects
High Energy Physics - Theory
Abstract

We consider Type IIB compactifications on an isotropic torus $T^6$ threaded by geometric and non geometric fluxes. For this particular setup we apply supervised machine learning techniques, namely an artificial neural network coupled to a genetic algorithm, in order to obtain more than sixty thousand flux configurations yielding to a scalar potential with at least one critical point. We observe that both stable AdS vacua with large moduli masses and small vacuum energy as well as unstable dS vacua with small tachyonic mass and large energy are absent, in accordance to the Refined de Sitter Conjecture. Moreover, by considering a hierarchy among fluxes, we observe that perturbative solutions with small values for the vacuum energy and moduli masses are favored, as well as scenarios in which the lightest modulus mass is much greater than the corresponding AdS vacuum scale. Finally we apply some results on Random Matrix Theory to conclude that the most probable mass spectrum derived from this string setup is that satisfying the Refined de Sitter and AdS scale conjectures., Comment: 30 pages, 14 Figures. (v2) References added

Published
2020

10. Loss-of-entanglement prediction of a controlled-PHASE gate in the framework of steepest-entropy-ascent quantum thermodynamics

Author

Montañez-Barrera, J. A., Damian-Ascencio, Cesar E., von Spakovsky, Michael R., and Cano-Andrade, Sergio

Subjects
Quantum Physics, Computer Science - Emerging Technologies, Physics - Atomic Physics, Physics - Computational Physics
Abstract

As has been shown elsewhere, a reasonable model of the loss of entanglement or correlation that occurs in quantum computations is one which assumes that they can effectively be predicted by a framework that presupposes the presence of irreversibilities internal to the system. It is based on the steepest-entropy-ascent principle and is used here to reproduce the behavior of a controlled-PHASE gate in good agreement with experimental data. The results show that the loss of entanglement predicted is related to the irreversibilities in a nontrivial way, providing a possible alternative approach that warrants exploration to that conventionally used to predict the loss of entanglement. The results provide a means for understanding this loss in quantum protocols from a nonequilibrium thermodynamic standpoint. This framework permits the development of strategies for extending either the maximum fidelity of the computation or the entanglement time.

Published
2020
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13. PennyLane: Automatic differentiation of hybrid quantum-classical computations

Author

Bergholm, Ville, Izaac, Josh, Schuld, Maria, Gogolin, Christian, Ahmed, Shahnawaz, Ajith, Vishnu, Alam, M. Sohaib, Alonso-Linaje, Guillermo, AkashNarayanan, B., Asadi, Ali, Arrazola, Juan Miguel, Azad, Utkarsh, Banning, Sam, Blank, Carsten, Bromley, Thomas R, Cordier, Benjamin A., Ceroni, Jack, Delgado, Alain, Di Matteo, Olivia, Dusko, Amintor, Garg, Tanya, Guala, Diego, Hayes, Anthony, Hill, Ryan, Ijaz, Aroosa, Isacsson, Theodor, Ittah, David, Jahangiri, Soran, Jain, Prateek, Jiang, Edward, Khandelwal, Ankit, Kottmann, Korbinian, Lang, Robert A., Lee, Christina, Loke, Thomas, Lowe, Angus, McKiernan, Keri, Meyer, Johannes Jakob, Montañez-Barrera, J. A., Moyard, Romain, Niu, Zeyue, O'Riordan, Lee James, Oud, Steven, Panigrahi, Ashish, Park, Chae-Yeun, Polatajko, Daniel, Quesada, Nicolás, Roberts, Chase, Sá, Nahum, Schoch, Isidor, Shi, Borun, Shu, Shuli, Sim, Sukin, Singh, Arshpreet, Strandberg, Ingrid, Soni, Jay, Száva, Antal, Thabet, Slimane, Vargas-Hernández, Rodrigo A., Vincent, Trevor, Vitucci, Nicola, Weber, Maurice, Wierichs, David, Wiersema, Roeland, Willmann, Moritz, Wong, Vincent, Zhang, Shaoming, and Killoran, Nathan

Subjects
Quantum Physics, Computer Science - Emerging Technologies, Computer Science - Machine Learning, Physics - Computational Physics
Abstract

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications., Comment: Code available at https://github.com/XanaduAI/pennylane/ . Significant contributions to the code (new features, new plugins, etc.) will be recognized by the opportunity to be a co-author on this paper

Published
2018

14. Reported Rationales for HPV Vaccination vs. Non-Vaccination among Undergraduate and Medical Students in South Carolina

Author

Barrera, J., Greene, S., Petyak, E., Kenneson, S., McGill, E., Howell, H., Billing, D., Taylor, S., Ewing, A., and Cull, J.

Abstract

Objective: We sought to identify factors that influence Human Papillomavirus (HPV) vaccination rates in individuals at two higher education institutions in South Carolina (SC). Participants: We surveyed 1007 students with a mean age and standard deviation of 20.3 ± 3.3 from September 2018 to December 2018. Methods: Participants answered 13 questions, assessing HPV vaccination rates, demographics, and rationales for vaccination vs. non-vaccination. Results: Of 1007 respondents, 700 received HPV vaccination, 165 were unvaccinated, 75 received partial vaccination and 138 were uncertain. Commonalities in HPV vaccination existed between females (p = 0.037), individuals who received standard childhood vaccinations (p = 0.04), and those not native-born in SC (p < 0.001). Of non-vaccinated individuals, 37% "never thought about vaccination," 32% did not perceive a need for vaccination, and 31% reported vaccine safety as reasons for not receiving the vaccine. Conclusions: Promotion of HPV vaccination may benefit from targeting SC natives, males, and individuals who are under-vaccinated or unvaccinated.

Published
2021
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15. Cambios en la masa ósea en una población infantil con diabetes mellitus tipo 1. Estudio longitudinal

Author

Vázquez Gámez MA, Bocio Nuñez J, Bermudez de la Vega JA, Bernal Cerrato S, Giner García M, Miranda García MJ, Hernandez Cruz B, Olmo-Montes J, Barrera Barrera J, and Montoya García MJ

Subjects
diabetes mellitus tipo 1, infancia, densidad mineral ósea, recambio óseo, estudio longitudinal, Medicine, Osteopathy, RZ301-397.5
Abstract

Objetivo: Evaluar, a lo largo de un seguimiento de 79,2 meses, el comportamiento de la densidad mineral ósea (DMO) determinada mediante Densitometría Axial Computarizada (DXA), la densidad mineral ósea volumétrica (DMOvol) y su relación con los datos antropométricos, junto con los parámetros relativos al metabolismo óseo (calcio, fósforo, fosfatasa alcalina, parathormona (PTH) y vitamina D (25-OH-D3)) en una población infantil con Diabetes Mellitus Tipo 1 (DM1) sin complicaciones microvasculares y un grupo control de referencia de similares características. Material y métodos: Inicialmente, se realizó un estudio transversal en 40 niños diabéticos (edad media 9,4±2,8 años) y 108 controles (9,3±1,5 años) para valorar las posibles diferencias entre ambas poblaciones. 26 pacientes del grupo diabético inicial, fueron reevaluados tras 79,2 meses de seguimiento. Resultados: Se observó que, al inicio, la masa ósea fue similar en los diabéticos y controles. Después del seguimiento, la DMO de los niños diabéticos era muy inferior a la esperada en población infantil no diabética. El peso, la altura y el Índice de Masa Corporal (IMC) siguieron el mismo patrón que la DMO. Los valores de calcio, fósforo, fosfatasa alcalina, PTH y vitamina D, aunque en rango de normalidad, fueron más bajos que en los controles. La fosfatasa alcalina no se incrementó en el periodo puberal. Conclusiones: El presente estudio demuestra que los niños y adolescentes con un diagnóstico reciente de DM1 tienen una DMO normal. Sin embargo, con el paso del tiempo, y sobre todo durante la adolescencia, muestran una menor ganancia de masa ósea y alteraciones en los parámetros de recambio óseo.

Published
2022
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18. Identifying and preventing burnout in young oncologists, an overwhelming challenge in the COVID-19 era: a study of the Spanish Society of Medical Oncology (SEOM)

Author

Jiménez-Labaig, P., Pacheco-Barcia, V., Cebrià, A., Gálvez, F., Obispo, B., Páez, D., Quílez, A., Quintanar, T., Ramchandani, A., Remon, J., Rogado, J., Sánchez, D.A., Sánchez-Cánovas, M., Sanz-García, E., Sesma, A., Tarazona, N., Cotés, A., González, E., Bosch-Barrera, J., Fernández, A., Felip, E., Vera, R., Rodríguez-Lescure, Á., and Élez, E.

Published
2021
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21. The One-Node Approach to Implement Smart Grid Functions for Residential Loads

Author

Saleh, S. A., McSporran, E. C., Barrera, J. L. Cardenas, Castillo-Guerra, E., and Diduch, C. P.

Abstract

This paper presents and tests a one-node method for implementing smart grid functions to operate residential loads. The proposed method is developed based on adjusting the power demands of residential loads to achieve a desired load-demand profile at the supply node. A desired load-demand profile is set based on operating thermostatically controlled appliances (TCAs) in the target residential loads. Smart grid functions are implemented to operate TCAs so that thermal energy is stored during the daily off-peak-demand hours. This stored thermal energy is discharged during daily peak-demand hours in order to reduce power demands of residential loads during these hours. The command power assigned to each TCA controller (set to implement smart grid functions) is initiated using a modified-profile for residential load hosting these TCAs. The one-node method is implemented and tested for a university campus that has 45 buildings. Each building has central central heating units and water heaters, and some buildings have central air conditioner units. Tests are performed for different seasons, where power demands of campus buildings are controlled by peak-demand management (as a smart grid function). Test results show the accuracy and simplicity of the one-node method to assign command values for each building to ensure reduced power losses and improved voltage.

Published
2024
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26. University Classroom Teaching Innovations: A Meta-Analysis Study.

Author

Villar, L. M., Barrera, J. M., Betancourt, F., Camunez, J. A., Casanueva, C., Correa, J., Garcia, E., Gomez, I., Hervas, C., Jimenez, M., Maldonado, M. D., de Manuel, E., Martinez, J. L., Medianero, J. M., Morales, J. A., Munoz, U. J., Navarro, J., Ortega, A., Perez, M. D., and Perianez, R.

Abstract

This study explored whether university students' perceptions of the social environment are influenced by professors' classroom teaching innovations. Data from 559 university students in 13 innovative disciplines within 11 schools at the University of Seville, Spain, were used. First, factor analytic procedures with varimax rotation were used to generate a seven-factor solution that accounted for 59% of the variance. Second, using hierarchical linear modeling, it was found that the average student's perception of interest is also influenced by prior knowledge. Implications for further research on university teaching and innovation assessment are discussed. Two appendixes contain sample questionnaires. (SLD)

Published
2002

28. P1.01-01 Frequency of Germline Mutations in Patients with Non-small Cell Lung Cancer (NSCLC) Harboring Actionable DRIVER Alterations (G-DRIVER)

Author

Jové, M., primary, Gausachs, M., additional, Bosch-Barrera, J., additional, Carcereny, E., additional, Teule, A., additional, Mosteiro, M., additional, Pineda, M., additional, Tornero, E., additional, Palmero, R., additional, Vilarino, N., additional, Ruffinelli, J.C., additional, Mesia, C., additional, Saldaña, J., additional, Cuellar, A., additional, Brenes, J., additional, Hernandez, A., additional, Fina, C., additional, Brunet, J., additional, Domenech, M., additional, Moran, T., additional, Sais, E., additional, Lazaro, C., additional, and Nadal, E., additional

Published
2023
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29. P1.01-04 Clinical and Genetic Characteristics in Younger vs Older Lung Cancer Patients

Author

Candal-Pedreira, C., primary, Ruano-Ravina, A., additional, Calvo de Juan, V., additional, Cobo, M., additional, Trigo, J.M., additional, Carcereny, E., additional, Cucurull, M., additional, López Castro, R., additional, Solís García, E., additional, Sánchez-Gastaldo, A., additional, Massutí, B., additional, Rodríguez-Abreu, D., additional, Estival, A., additional, Guirado Risueño, M., additional, Rey-Brandariz, J., additional, Pamiés Ramón, M., additional, García Campelo, R., additional, Alonso-Jáudenes, G., additional, Camps, C., additional, del Barco Morillo, E., additional, González Ojea, C., additional, Dómine, M., additional, Sánchez-Hernández, A., additional, Bosch-Barrera, J., additional, Sala González, M.Á., additional, and Provencio, M., additional

Published
2023
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30. P1.27-07 BRAF Mutations Identify Non-small-Cell Lung Cancer Patients Who Benefit from Neoadjuvant Chemo-Immunotherapy

Author

Robado de Lope, L., primary, Nadal, E., additional, Calvo, V., additional, González-Larriba, J.L., additional, Insa, A., additional, Martinez-Marti, A., additional, Massutí, B., additional, González, C., additional, Serna Blasco, R., additional, Bosch-Barrera, J., additional, Garcia-Campelo, M.R., additional, Ponce, S., additional, Reguart, N., additional, Bernabé, R., additional, Cobo, M., additional, De Castro, J., additional, Dómine, M., additional, Camps, C., additional, Barneto, I., additional, López-Vivanco, G., additional, Majem, M., additional, Rodríguez-Abreu, D., additional, Palmero, R., additional, Cruz-Bermudez, A., additional, Romero, A., additional, and Provencio, M., additional

Published
2023
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33. EP02.01-05 Using Silibinin to Target STAT3-Positive Reactive Astrocytes for the Treatment of NSCLC Brain Metastases

Author

Bosch-Barrera, J., primary, Teixidor, E., additional, Sanvisens, A., additional, Puigdemont, M., additional, Trallero, J., additional, Vidal Vila, A., additional, Osca, G., additional, Verdaguer Roberte, M., additional, Priego, N., additional, Sais, E., additional, Hernández Martínez, A., additional, Sabaté Ortega, J., additional, Cuyàs, E., additional, Verdura, S., additional, Puig, J., additional, Pineda, V., additional, Martí-Navas, M., additional, Calvo, A., additional, Menendez, J.A., additional, Marcos-Gragera, R., additional, and Valiente, M., additional

Published
2023
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41. Pomalidomide in the treatment of multiple myeloma: design, development and place in therapy

Author

Rios-Tamayo R, Martín-García A, Alarcón-Payer C, Sánchez-Rodríguez D, Guardia AM, García Collado CG, Jiménez Morales A, Jurado Chacón M, and Cabeza Barrera J

Subjects
Multiple Myeloma, Pomalidomide, Therapeutics. Pharmacology, RM1-950
Abstract

Rafael Ríos-Tamayo,1–4 Agustín Martín-García,5,6 Carolina Alarcón-Payer,5 Dolores Sánchez-Rodríguez,1,7 Ana María del Valle Díaz de la Guardia,5 Carlos Gustavo García Collado,5 Alberto Jiménez Morales,5 Manuel Jurado Chacón,1–4 José Cabeza Barrera4,5 1Monoclonal Gammopathies Unit, 2Department of Hematology, University Hospital Virgen de las Nieves, Granada, Spain; 3Genomic Oncology Area, GENYO, Center for Genomics and Oncological Research: Pfizer/University of Granada/Andalusian Regional Government, PTS, Granada, Spain; 4Instituto de Investigación Biosanitaria de Granada (Ibs.GRANADA), Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain; 5Department of Pharmacy, 6Clinical Trials Unit, University Hospital Virgen de las Nieves, Granada, Spain; 7FIBAO, Granada, Spain Abstract: Multiple myeloma is a very heterogeneous disease with variable survival. Despite recent progress and the widespread use of new agents, patients with relapsed and refractory disease have a poor outcome. Immunomodulatory drugs play a key role in both the front-line and the relapsed/refractory setting. The combination of pomalidomide (POM) and dexamethasone is safe and effective in relapsed and refractory patients, even in those with high-risk cytogenetic features. Furthermore, it can be used in most patients without the need to adjust according to the degree of renal failure. In order to further improve the results, POM-based triplet therapies are currently used. This article highlights the most relevant issues of POM and POM-based combinations in the relapsed/refractory multiple myeloma setting, from a pharmacological and clinical point of view. Keywords: multiple myeloma, pomalidomide, triplet therapy, dexamethasone

Published
2017

44. Perioperative Pembrolizumab for Early-Stage Non-Small-Cell Lung Cancer

Author

Heather, W, Moishe, L, Terufumi, K, Masahiro, T, Se-Hoon, L, Shugeng, G, Ke-Neng, C, Christophe, D, Margarita, M, Ekkehard, E, Gastón L, M, Olivier, B, Delvys, R, Jamie E, C, Silvia, N, Jing, Y, Steven M, K, Ayman, S, Spicer, D Marcelo Tatangelo, J, Flores, M, Pastor, A, Puig, J, Martinengo, G, Varela, M, Brocca, C, Wong, M, Hui, R, Dooms, C, Vansteenkiste, J, Demedts, I, Sibille, A, Surmont, V, Deschepper, K, Lambrechts, M, Dias, J, Rafael Martins De Marchi, P, Alves, G, Henrique Araujo, L, Matias, D, Chaves, F, Franke, F, Teixeira, C, Tabacof, J, Faria, L, Morbeck, I, Henrique Cronemberger, E, Lima, I, Sardenberg, R, de Paiva Junior, T, Dutra, C, Luiz Guimaraes, J, Begin, P, Langleben, A, Liu, G, Liberman, M, Spicer, J, Gao, S, Zhao, G, Jiang, T, Yan, X, Hu, J, Chen, J, Tan, L, Wang, Q, Li, S, Chen, K, Yang, Y, Bai, J, Ma, S, Chen, H, Chen, Q, Wang, W, Zhang, L, Zhu, Y, Vanakesa, T, Zasadny, X, Duchemann, B, Girard, N, Bylicki, O, Berard, H, Thiberville, L, Mennecier, B, Mazieres, J, Eigendorff, E, Bonnet, R, Fix, P, Reck, M, Rittmeyer, A, Reinacher-Schick, A, Wehler, T, Lehmann, M, Serke, M, Wesseler, C, Täuscher, D, Lang, S, Wermke, M, Grohe, C, Wirtz, H, Kollmeier, J, Ritgen, M, Mueller, A, Frohling, K, Vogel, G, Faehling, M, Cuffe, S, Collins, D, Delmonte, A, Gilli, M, Piantedosi, F, Ogliari, F, Bulotta, A, Gregorc, V, Gianni, L, Grisanti, S, Intagliata, S, Roca, E, Ferrari, V, Berruti, A, Cortinovis, D, Lo Russo, G, Ferrara, R, Garassino, M, Rita Migliorino, M, Novello, S, Santoro, A, Signorelli, D, Tsuboi, M, Okada, M, Kato, T, Nishio, W, Kuroda, H, Shimizu, J, Sakao, Y, Sugio, K, Horinouchi, H, Takamochi, K, Saji, H, Tanaka, F, Ikeda, N, Muto, S, Shio, Y, Suzuki, H, Hegmane, A, Cicenas, S, Zemaitis, M, Kek Pang, Y, Yew Heng, F, Leong Yu, K, Lowczak, A, Makles, K, Bryl, M, Zurawski, B, Pawlak, I, Han, J, Lee, S, Kim, J, Yong Shim, B, Cebotaru, C, Ganea, D, Scheusan, R, Ciurescu, I, Mazilu, L, Ungureanu, A, Gal, C, Ciubotaru, E, Iordan, I, Berceanu-Ion, R, Ciuleanu, T, Laktionov, K, Karaseva, N, Smagina, M, Luft, A, Afanasyev, S, Nesterova, A, Levchenko, E, Arkhipov, A, Fedenko, A, Ruff, P, Jacobs, C, Fourie, S, Carcereny, E, Calles Blanco, A, Rodriguez Abreu, D, Majem Tarruella, M, Bosch Barrera, J, Bernabe Caro, R, Nadal Alforja, E, Martnez Marti, A, Liao, B, Huang, H, Chiu, C, Wang, C, Tsai, C, Voitko, N, Kryzhanivska, A, Kolesnik, O, Levenko, O, Bondarenko, I, Trukhin, D, Ursol, G, Paramonov, V, Sokur, I, Khan, S, Arora, A, Goranov, B, Greystoke, A, Ahmed, S, Pope, T, O'Brien, M, Charu, V, Cobb, P, Costin, D, Weksler, B, Schumacher, L, Finley, G, Furqan, M, Gentzler, R, Misleh, J, Guarino, M, Halmos, B, Keresztes, R, Jain, K, Yan Lou, Y, Molina, J, Liu-Dumlao, T, Zhao, Q, Niu, J, Taysir Hammoud, Z, Rybkin, I, Cuevo, R, Fernando, H, Schiller, J, Srkalovic, G, Koontz, M, Stampleman, L, Anderson, I, Villaruz, L, Wang, S, Komiya, T, Jain, S, Starodub, A, Wakelee, H, Kishor Ganti, A, Ernani, V, Kristedja, T, O'Day, S, Radhi, S, Sangal, A, Duvivier, H, Rich, P, Kazmi, S, Pollock, T, Chaft, J, Rathnasabapathy, C, Savvides, P, Costas, K, Kaywin, P, Villalona-Calero, M, Alekshun, T, Rao, S, Siegel, R, Wakelee, Heather, Liberman, Moishe, Kato, Terufumi, Tsuboi, Masahiro, Lee, Se-Hoon, Gao, Shugeng, Chen, Ke-Neng, Dooms, Christophe, Majem, Margarita, Eigendorff, Ekkehard, Martinengo, Gastón L, Bylicki, Olivier, Rodríguez-Abreu, Delvys, Chaft, Jamie E, Novello, Silvia, Yang, Jing, Keller, Steven M, Samkari, Ayman, Jonathan D Marcelo Tatangelo, Marcos Flores, Andrea Pastor, Juan Puig, Gaston Martinengo, Mirta Varela, Carlos Brocca, Mark Wong, Rina Hui, Christophe Dooms, Johan Vansteenkiste, Ingel Demedts, Anne Sibille, Veerle Surmont, Koenraad Deschepper, Marc Lambrechts, Josiane Dias, Pedro Rafael Martins De Marchi, Gustavo Alves, Luiz Henrique Araujo, Danielli Matias, Fabio Chaves, Fabio Franke, Carlos Teixeira, Jacques Tabacof, Luiza Faria, Igor Morbeck, Eduardo Henrique Cronemberger, Iane Lima, Rodrigo Sardenberg, Tadeu de Paiva Junior, Carolina Dutra, Jose Luiz Guimaraes, Paul Begin, Adrian Langleben, Geoffrey Liu, Moishe Liberman, Jonathan Spicer, Shugeng Gao, Guofang Zhao, Tao Jiang, Xiaolong Yan, Jian Hu, Jun Chen, Lijie Tan, Qun Wang, Shanqing Li, Keneng Chen, Yue Yang, Jie Bai, Shaohua Ma, Haiquan Chen, Qixun Chen, Wenxiang Wang, Lanjun Zhang, Yuming Zhu, Tonu Vanakesa, Xavier Zasadny, Boris Duchemann, Nicolas Girard, Olivier Bylicki, Henri Berard, Luc Thiberville, Bertrand Mennecier, Julien Mazieres, Ekkehard Eigendorff, Reiner Bonnet, Peter Fix, Martin Reck, Achim Rittmeyer, Anke Reinacher-Schick, Thomas Wehler, Markus Lehmann, Monika Serke, Claas Wesseler, Dagmar Täuscher, Susanne Lang, Martin Wermke, Christian Grohe, Hubert Wirtz, Jens Kollmeier, Mathias Ritgen, Annette Mueller, Klaus-Peter Frohling, Gunther Vogel, Martin Faehling, Sinead Cuffe, Dearbhaile Collins, Angelo Delmonte, Marina Gilli, Francovito Piantedosi, Francesca Ogliari, Alessandra Bulotta, Vanesa Gregorc, Luca Gianni, Salvatore Grisanti, Salvatore Intagliata, Elisa Roca, Vittorio Ferrari, Alfredo Berruti, Diego Cortinovis, Giuseppe Lo Russo, Roberto Ferrara, Marina Garassino, Maria Rita Migliorino, Silvia Novello, Armando Santoro, Diego Signorelli, Masahiro Tsuboi, Morihito Okada, Terufumi Kato, Wataru Nishio, Hiroaki Kuroda, Junichi Shimizu, Yukinori Sakao, Kenji Sugio, Hidehito Horinouchi, Kazuya Takamochi, Hisashi Saji, Fumihiro Tanaka, Norihiko Ikeda, Satoshi Muto, Yutaka Shio, Hiroyuki Suzuki, Alinta Hegmane, Saulius Cicenas, Marius Zemaitis, Yong Kek Pang, Fook Yew Heng, Kong Leong Yu, Anna Lowczak, Krytsyna Makles, Maciej Bryl, Bogdan Zurawski, Ireneusz Pawlak, Ji-Youn Han, Se-Hoon Lee, Jhingook Kim, Byoung Yong Shim, Cristina Cebotaru, Doina Ganea, Roxana Scheusan, Ioana Ciurescu, Laura Mazilu, Andrei Ungureanu, Cristian Gal, Elena Ciubotaru, Ingrid Iordan, Radu Berceanu-Ion, Tudor Ciuleanu, Konstantin Laktionov, Nina Karaseva, Maria Smagina, Alexander Luft, Sergey Afanasyev, Alfiya Nesterova, Evgeny Levchenko, Alexander Arkhipov, Alexander Fedenko, Paul Ruff, Conrad Jacobs, Samuel Fourie, Enric Carcereny, Antonio Calles Blanco, Delvys Rodriguez Abreu, Margarita Majem Tarruella, Joaquim Bosch Barrera, Reyes Bernabe Caro, Ernest Nadal Alforja, Alex Martnez Marti, Bin-Chi Liao, Hsu-Ching Huang, Chao-Hua Chiu, Chin-Chou Wang, Chen-Liang Tsai, Nataliia Voitko, Anna Kryzhanivska, Olena Kolesnik, Oleh Levenko, Oleksii Kolesnik, Igor Bondarenko, Dmytro Trukhin, Grygorii Ursol, Viktor Paramonov, Iryna Sokur, Sarah Khan, Arvind Arora, Bojidar Goranov, Alastair Greystoke, Samreen Ahmed, Tony Pope, Mary O'Brien, Veena Charu, Patrick Cobb, Dan Costin, Benny Weksler, Lana Schumacher, Gene Finley, Muhammad Furqan, Ryan Gentzler, Jamal Misleh, Michael Guarino, Balazs Halmos, Roger Keresztes, Kirti Jain, Yan Yan Lou, Julian Molina, Theresa Liu-Dumlao, Qing Zhao, Jiaxin Niu, Zane Taysir Hammoud, Igor Rybkin, Raymund Cuevo, Hiran Fernando, Joan Schiller, Gordan Srkalovic, Michael Koontz, Laura Stampleman, Ian Anderson, Liza Villaruz, Sarah Wang, Takefumi Komiya, Sushil Jain, Alexander Starodub, Heather Wakelee, Apar Kishor Ganti, Vinicius Ernani, Timothy Kristedja, Steven O'Day, Saba Radhi, Ashish Sangal, Herbert Duvivier, Patricia Rich, Shayma Kazmi, Theodore Pollock, Jamie Chaft, Chenthilmurugan Rathnasabapathy, Panayiotis Savvides, Kimberly Costas, Paul Kaywin, Miguel Villalona-Calero, Todd Alekshun, Kevin Chen, Suman Rao, Robert Siegel, Heather, W, Moishe, L, Terufumi, K, Masahiro, T, Se-Hoon, L, Shugeng, G, Ke-Neng, C, Christophe, D, Margarita, M, Ekkehard, E, Gastón L, M, Olivier, B, Delvys, R, Jamie E, C, Silvia, N, Jing, Y, Steven M, K, Ayman, S, Spicer, D Marcelo Tatangelo, J, Flores, M, Pastor, A, Puig, J, Martinengo, G, Varela, M, Brocca, C, Wong, M, Hui, R, Dooms, C, Vansteenkiste, J, Demedts, I, Sibille, A, Surmont, V, Deschepper, K, Lambrechts, M, Dias, J, Rafael Martins De Marchi, P, Alves, G, Henrique Araujo, L, Matias, D, Chaves, F, Franke, F, Teixeira, C, Tabacof, J, Faria, L, Morbeck, I, Henrique Cronemberger, E, Lima, I, Sardenberg, R, de Paiva Junior, T, Dutra, C, Luiz Guimaraes, J, Begin, P, Langleben, A, Liu, G, Liberman, M, Spicer, J, Gao, S, Zhao, G, Jiang, T, Yan, X, Hu, J, Chen, J, Tan, L, Wang, Q, Li, S, Chen, K, Yang, Y, Bai, J, Ma, S, Chen, H, Chen, Q, Wang, W, Zhang, L, Zhu, Y, Vanakesa, T, Zasadny, X, Duchemann, B, Girard, N, Bylicki, O, Berard, H, Thiberville, L, Mennecier, B, Mazieres, J, Eigendorff, E, Bonnet, R, Fix, P, Reck, M, Rittmeyer, A, Reinacher-Schick, A, Wehler, T, Lehmann, M, Serke, M, Wesseler, C, Täuscher, D, Lang, S, Wermke, M, Grohe, C, Wirtz, H, Kollmeier, J, Ritgen, M, Mueller, A, Frohling, K, Vogel, G, Faehling, M, Cuffe, S, Collins, D, Delmonte, A, Gilli, M, Piantedosi, F, Ogliari, F, Bulotta, A, Gregorc, V, Gianni, L, Grisanti, S, Intagliata, S, Roca, E, Ferrari, V, Berruti, A, Cortinovis, D, Lo Russo, G, Ferrara, R, Garassino, M, Rita Migliorino, M, Novello, S, Santoro, A, Signorelli, D, Tsuboi, M, Okada, M, Kato, T, Nishio, W, Kuroda, H, Shimizu, J, Sakao, Y, Sugio, K, Horinouchi, H, Takamochi, K, Saji, H, Tanaka, F, Ikeda, N, Muto, S, Shio, Y, Suzuki, H, Hegmane, A, Cicenas, S, Zemaitis, M, Kek Pang, Y, Yew Heng, F, Leong Yu, K, Lowczak, A, Makles, K, Bryl, M, Zurawski, B, Pawlak, I, Han, J, Lee, S, Kim, J, Yong Shim, B, Cebotaru, C, Ganea, D, Scheusan, R, Ciurescu, I, Mazilu, L, Ungureanu, A, Gal, C, Ciubotaru, E, Iordan, I, Berceanu-Ion, R, Ciuleanu, T, Laktionov, K, Karaseva, N, Smagina, M, Luft, A, Afanasyev, S, Nesterova, A, Levchenko, E, Arkhipov, A, Fedenko, A, Ruff, P, Jacobs, C, Fourie, S, Carcereny, E, Calles Blanco, A, Rodriguez Abreu, D, Majem Tarruella, M, Bosch Barrera, J, Bernabe Caro, R, Nadal Alforja, E, Martnez Marti, A, Liao, B, Huang, H, Chiu, C, Wang, C, Tsai, C, Voitko, N, Kryzhanivska, A, Kolesnik, O, Levenko, O, Bondarenko, I, Trukhin, D, Ursol, G, Paramonov, V, Sokur, I, Khan, S, Arora, A, Goranov, B, Greystoke, A, Ahmed, S, Pope, T, O'Brien, M, Charu, V, Cobb, P, Costin, D, Weksler, B, Schumacher, L, Finley, G, Furqan, M, Gentzler, R, Misleh, J, Guarino, M, Halmos, B, Keresztes, R, Jain, K, Yan Lou, Y, Molina, J, Liu-Dumlao, T, Zhao, Q, Niu, J, Taysir Hammoud, Z, Rybkin, I, Cuevo, R, Fernando, H, Schiller, J, Srkalovic, G, Koontz, M, Stampleman, L, Anderson, I, Villaruz, L, Wang, S, Komiya, T, Jain, S, Starodub, A, Wakelee, H, Kishor Ganti, A, Ernani, V, Kristedja, T, O'Day, S, Radhi, S, Sangal, A, Duvivier, H, Rich, P, Kazmi, S, Pollock, T, Chaft, J, Rathnasabapathy, C, Savvides, P, Costas, K, Kaywin, P, Villalona-Calero, M, Alekshun, T, Rao, S, Siegel, R, Wakelee, Heather, Liberman, Moishe, Kato, Terufumi, Tsuboi, Masahiro, Lee, Se-Hoon, Gao, Shugeng, Chen, Ke-Neng, Dooms, Christophe, Majem, Margarita, Eigendorff, Ekkehard, Martinengo, Gastón L, Bylicki, Olivier, Rodríguez-Abreu, Delvys, Chaft, Jamie E, Novello, Silvia, Yang, Jing, Keller, Steven M, Samkari, Ayman, Jonathan D Marcelo Tatangelo, Marcos Flores, Andrea Pastor, Juan Puig, Gaston Martinengo, Mirta Varela, Carlos Brocca, Mark Wong, Rina Hui, Christophe Dooms, Johan Vansteenkiste, Ingel Demedts, Anne Sibille, Veerle Surmont, Koenraad Deschepper, Marc Lambrechts, Josiane Dias, Pedro Rafael Martins De Marchi, Gustavo Alves, Luiz Henrique Araujo, Danielli Matias, Fabio Chaves, Fabio Franke, Carlos Teixeira, Jacques Tabacof, Luiza Faria, Igor Morbeck, Eduardo Henrique Cronemberger, Iane Lima, Rodrigo Sardenberg, Tadeu de Paiva Junior, Carolina Dutra, Jose Luiz Guimaraes, Paul Begin, Adrian Langleben, Geoffrey Liu, Moishe Liberman, Jonathan Spicer, Shugeng Gao, Guofang Zhao, Tao Jiang, Xiaolong Yan, Jian Hu, Jun Chen, Lijie Tan, Qun Wang, Shanqing Li, Keneng Chen, Yue Yang, Jie Bai, Shaohua Ma, Haiquan Chen, Qixun Chen, Wenxiang Wang, Lanjun Zhang, Yuming Zhu, Tonu Vanakesa, Xavier Zasadny, Boris Duchemann, Nicolas Girard, Olivier Bylicki, Henri Berard, Luc Thiberville, Bertrand Mennecier, Julien Mazieres, Ekkehard Eigendorff, Reiner Bonnet, Peter Fix, Martin Reck, Achim Rittmeyer, Anke Reinacher-Schick, Thomas Wehler, Markus Lehmann, Monika Serke, Claas Wesseler, Dagmar Täuscher, Susanne Lang, Martin Wermke, Christian Grohe, Hubert Wirtz, Jens Kollmeier, Mathias Ritgen, Annette Mueller, Klaus-Peter Frohling, Gunther Vogel, Martin Faehling, Sinead Cuffe, Dearbhaile Collins, Angelo Delmonte, Marina Gilli, Francovito Piantedosi, Francesca Ogliari, Alessandra Bulotta, Vanesa Gregorc, Luca Gianni, Salvatore Grisanti, Salvatore Intagliata, Elisa Roca, Vittorio Ferrari, Alfredo Berruti, Diego Cortinovis, Giuseppe Lo Russo, Roberto Ferrara, Marina Garassino, Maria Rita Migliorino, Silvia Novello, Armando Santoro, Diego Signorelli, Masahiro Tsuboi, Morihito Okada, Terufumi Kato, Wataru Nishio, Hiroaki Kuroda, Junichi Shimizu, Yukinori Sakao, Kenji Sugio, Hidehito Horinouchi, Kazuya Takamochi, Hisashi Saji, Fumihiro Tanaka, Norihiko Ikeda, Satoshi Muto, Yutaka Shio, Hiroyuki Suzuki, Alinta Hegmane, Saulius Cicenas, Marius Zemaitis, Yong Kek Pang, Fook Yew Heng, Kong Leong Yu, Anna Lowczak, Krytsyna Makles, Maciej Bryl, Bogdan Zurawski, Ireneusz Pawlak, Ji-Youn Han, Se-Hoon Lee, Jhingook Kim, Byoung Yong Shim, Cristina Cebotaru, Doina Ganea, Roxana Scheusan, Ioana Ciurescu, Laura Mazilu, Andrei Ungureanu, Cristian Gal, Elena Ciubotaru, Ingrid Iordan, Radu Berceanu-Ion, Tudor Ciuleanu, Konstantin Laktionov, Nina Karaseva, Maria Smagina, Alexander Luft, Sergey Afanasyev, Alfiya Nesterova, Evgeny Levchenko, Alexander Arkhipov, Alexander Fedenko, Paul Ruff, Conrad Jacobs, Samuel Fourie, Enric Carcereny, Antonio Calles Blanco, Delvys Rodriguez Abreu, Margarita Majem Tarruella, Joaquim Bosch Barrera, Reyes Bernabe Caro, Ernest Nadal Alforja, Alex Martnez Marti, Bin-Chi Liao, Hsu-Ching Huang, Chao-Hua Chiu, Chin-Chou Wang, Chen-Liang Tsai, Nataliia Voitko, Anna Kryzhanivska, Olena Kolesnik, Oleh Levenko, Oleksii Kolesnik, Igor Bondarenko, Dmytro Trukhin, Grygorii Ursol, Viktor Paramonov, Iryna Sokur, Sarah Khan, Arvind Arora, Bojidar Goranov, Alastair Greystoke, Samreen Ahmed, Tony Pope, Mary O'Brien, Veena Charu, Patrick Cobb, Dan Costin, Benny Weksler, Lana Schumacher, Gene Finley, Muhammad Furqan, Ryan Gentzler, Jamal Misleh, Michael Guarino, Balazs Halmos, Roger Keresztes, Kirti Jain, Yan Yan Lou, Julian Molina, Theresa Liu-Dumlao, Qing Zhao, Jiaxin Niu, Zane Taysir Hammoud, Igor Rybkin, Raymund Cuevo, Hiran Fernando, Joan Schiller, Gordan Srkalovic, Michael Koontz, Laura Stampleman, Ian Anderson, Liza Villaruz, Sarah Wang, Takefumi Komiya, Sushil Jain, Alexander Starodub, Heather Wakelee, Apar Kishor Ganti, Vinicius Ernani, Timothy Kristedja, Steven O'Day, Saba Radhi, Ashish Sangal, Herbert Duvivier, Patricia Rich, Shayma Kazmi, Theodore Pollock, Jamie Chaft, Chenthilmurugan Rathnasabapathy, Panayiotis Savvides, Kimberly Costas, Paul Kaywin, Miguel Villalona-Calero, Todd Alekshun, Kevin Chen, Suman Rao, and Robert Siegel

Abstract

BACKGROUND Among patients with resectable early-stage non-small-cell lung cancer (NSCLC), a perioperative approach that includes both neoadjuvant and adjuvant immune checkpoint inhibition may provide benefit beyond either approach alone. METHODS We conducted a randomized, double-blind, phase 3 trial to evaluate perioperative pembrolizumab in patients with early-stage NSCLC. Participants with resectable stage II, IIIA, or IIIB (N2 stage) NSCLC were assigned in a 1:1 ratio to receive neoadjuvant pembrolizumab (200 mg) or placebo once every 3 weeks, each of which was given with cisplatin-based chemotherapy for 4 cycles, followed by surgery and adjuvant pembrolizumab (200 mg) or placebo once every 3 weeks for up to 13 cycles. The dual primary end points were event-free survival (the time from randomization to the first occurrence of local progression that precluded the planned surgery, unresectable tumor, progression or recurrence, or death) and overall survival. Secondary end points included major pathological response, pathological complete response, and safety. RESULTS A total of 397 participants were assigned to the pembrolizumab group, and 400 to the placebo group. At the prespecified first interim analysis, the median follow-up was 25.2 months. Event-free survival at 24 months was 62.4% in the pembrolizumab group and 40.6% in the placebo group (hazard ratio for progression, recurrence, or death, 0.58; 95% confidence interval [CI], 0.46 to 0.72; P<0.001). The estimated 24-month overall survival was 80.9% in the pembrolizumab group and 77.6% in the placebo group (P = 0.02, which did not meet the significance criterion). A major pathological response occurred in 30.2% of the participants in the pembrolizumab group and in 11.0% of those in the placebo group (difference, 19.2 percentage points; 95% CI, 13.9 to 24.7; P<0.0001; threshold, P = 0.0001), and a pathological complete response occurred in 18.1% and 4.0%, respectively (difference, 14.2 percentage points

Published
2023

48. List of Contributors

Author

Belinha, J., primary, Bourantas, G.C., additional, Buttenschön, A., additional, Carriero, A., additional, Cerrolaza, M., additional, Checa, S., additional, Citarella, R., additional, Comellas, E., additional, Crapts, L.Y.D., additional, Cristea, A., additional, de las Casas, E.B., additional, Dinis, L., additional, Doblaré, M., additional, Doweidar, M.H., additional, Drasdo, D., additional, Gao, X., additional, Garzón-Alvarado, D.A., additional, Gefen, A., additional, Gerbino, S., additional, Giorgi, M., additional, Guerrero, J.A., additional, Halliday, I., additional, Harrevelt, S.D., additional, Hashemi, Z., additional, Joldes, G.R., additional, Kadem, L., additional, Kadri, O.E., additional, Lally, C., additional, Lauricella, M., additional, Li, S., additional, Lishchuk, S.V., additional, Lobovský, L., additional, Miller, K., additional, Moldovan, N.I., additional, Montessori, A., additional, Natal Jorge, R., additional, Neagu, A., additional, Neu, C.P., additional, Nino-Barrera, J., additional, Nino, G., additional, Nolan, D., additional, Papavassiliou, D.V., additional, Pereira, A., additional, Perrella, M., additional, Peyroteo, M., additional, Pham, N.H., additional, Pierce, D.M., additional, Poljak, D., additional, Pontrelli, G., additional, Quinlan, N., additional, Ramírez Martínez, A.M., additional, Ricken, T., additional, Ryan, J.K., additional, Sego, T.J., additional, Shahriari, S., additional, Shefelbine, S.J., additional, Silberschmidt, V., additional, Sozumert, E., additional, Spencer, T.J., additional, Succi, S., additional, Tovar, A., additional, Van Liedekerke, P., additional, Vermolen, F.J., additional, Voronov, R.S., additional, Weihs, D., additional, Wittek, A., additional, and Zahedi, A., additional

Published
2018
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