Search Results - "Terranova, Nadia"

Machine Learning in Modeling Disease Trajectory and Treatment Outcomes: An Emerging Enabler for Model‐Informed Precision Medicine by Terranova, Nadia, Venkatakrishnan, Karthik

“…The increasing breadth and depth of resolution in biological and clinical data, including ‐omics and real‐world data, requires advanced analytical techniques…”
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Bridging pharmacology and neural networks: A deep dive into neural ordinary differential equations by Losada, Idris Bachali, Terranova, Nadia

“…The advent of machine learning has led to innovative approaches in dealing with clinical data. Among these, Neural Ordinary Differential Equations (Neural…”
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Population pharmacokinetics of ATR inhibitor berzosertib in phase I studies for different cancer types by Terranova, Nadia, Jansen, Mendel, Falk, Martin, Hendriks, Bart S.

“…Purpose Berzosertib (formerly M6620) is the first-in-class inhibitor of ataxia–telangiectasia and Rad3-related protein, a key component of the DNA damage…”
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Fast screening of covariates in population models empowered by machine learning by Sibieude, Emeric, Khandelwal, Akash, Hesthaven, Jan S., Girard, Pascal, Terranova, Nadia

“…One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters…”
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Population pharmacokinetic model selection assisted by machine learning by Sibieude, Emeric, Khandelwal, Akash, Girard, Pascal, Hesthaven, Jan S., Terranova, Nadia

“…A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss…”
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Explainable machine learning prediction of edema adverse events in patients treated with tepotinib by Amato, Federico, Strotmann, Rainer, Castello, Roberto, Bruns, Rolf, Ghori, Vishal, Johne, Andreas, Berghoff, Karin, Venkatakrishnan, Karthik, Terranova, Nadia

“…Tepotinib is approved for the treatment of patients with non‐small‐cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most…”
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Model‐informed assessment of ethnic sensitivity and dosage justification for Asian populations in the global clinical development and use of cladribine tablets by Munafo, Alain, Terranova, Nadia, Li, Dandan, Liu, Ping, Venkatakrishnan, Karthik

“…Cladribine tablets have been approved in many countries for the treatment of patients with various forms of relapsing multiple sclerosis (MS). Cladribine has a…”
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Predicting disease activity in patients with multiple sclerosis: An explainable machine‐learning approach in the Mavenclad trials by Basu, Sreetama, Munafo, Alain, Ben‐Amor, Ali‐Frederic, Roy, Sanjeev, Girard, Pascal, Terranova, Nadia

“…Multiple sclerosis (MS) is among the most common autoimmune disabling neurological conditions of young adults and affects more than 2.3 million people…”
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Asia‐inclusive drug development leveraging principles of ICH E5 and E17 guidelines: Case studies illustrating quantitative clinical pharmacology as a foundational enabler by Lu, Hong, Klopp‐Schulze, Lena, Mukker, Jatinder Kaur, Li, Dandan, Kuroki, Yoshihiro, Bolleddula, Jayaprakasam, Terranova, Nadia, Goteti, Kosalaram, Gao, Wei, Strotmann, Rainer, Dong, Jennifer, Venkatakrishnan, Karthik

“…With the International Conference on Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) E17 guidelines in effect from 2018, the…”
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Pharmacometric modeling and machine learning analyses of prognostic and predictive factors in the JAVELIN Gastric 100 phase III trial of avelumab by Terranova, Nadia, French, Jonathan, Dai, Haiqing, Wiens, Matthew, Khandelwal, Akash, Ruiz‐Garcia, Ana, Manitz, Juliane, Heydebreck, Anja, Ruisi, Mary, Chin, Kevin, Girard, Pascal, Venkatakrishnan, Karthik

“…Avelumab (anti–PD‐L1) is an approved anticancer treatment for several indications. The JAVELIN Gastric 100 phase III trial did not meet its primary objective…”
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Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology by Terranova, Nadia, Girard, Pascal, Ioannou, Konstantinos, Klinkhardt, Ute, Munafo, Alain

“…Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of…”
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Tumor growth inhibition modeling in patients with second line biliary tract cancer and first line non‐small cell lung cancer based on bintrafusp alfa trials by Milenković‐Grišić, Ana‐Marija, Terranova, Nadia, Mould, Diane R., Vugmeyster, Yulia, Mrowiec, Thomas, Machl, Andreas, Girard, Pascal, Venkatakrishnan, Karthik, Khandelwal, Akash

“…This analysis aimed to quantify tumor dynamics in patients receiving either bintrafusp alfa (BA) or pembrolizumab, by population pharmacokinetic…”
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Modeling tumor size dynamics based on real‐world electronic health records and image data in advanced melanoma patients receiving immunotherapy by Courlet, Perrine, Abler, Daniel, Guidi, Monia, Girard, Pascal, Amato, Federico, Vietti Violi, Naik, Dietz, Matthieu, Guignard, Nicolas, Wicky, Alexandre, Latifyan, Sofiya, De Micheli, Rita, Jreige, Mario, Dromain, Clarisse, Csajka, Chantal, Prior, John O., Venkatakrishnan, Karthik, Michielin, Olivier, Cuendet, Michel A., Terranova, Nadia

“…The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy…”
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The Standard Output: A Tool‐Agnostic Modeling Storage Format by Terranova, Nadia, Smith, Mike K., Nordgren, Rikard, Comets, Emmanuelle, Lavielle, Marc, Harling, Kajsa, Hooker, Andrew C., Sarr, Celine, Mentré, France, Yvon, Florent, Swat, Maciej J.

“…The SO is based on a hierarchical structure defined to separate independent output information or results obtained from different modeling tasks (e.g.,…”
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Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities by Terranova, Nadia, Venkatakrishnan, Karthik, Benincosa, Lisa J.

“…The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed…”
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The Clinical Pharmacology of Cladribine Tablets for the Treatment of Relapsing Multiple Sclerosis by Hermann, Robert, Karlsson, Mats O., Novakovic, Ana M., Terranova, Nadia, Fluck, Markus, Munafo, Alain

“…Cladribine Tablets (MAVENCLAD ® ) are used to treat relapsing multiple sclerosis (MS). The recommended dose is 3.5 mg/kg, consisting of 2 annual courses, each…”
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Effects of Postponing Treatment in the Second Year of Cladribine Administration: Clinical Trial Simulation Analysis of Absolute Lymphocyte Counts and Relapse Rate in Patients with Relapsing-Remitting Multiple Sclerosis by Terranova, Nadia, Hicking, Christine, Dangond, Fernando, Munafo, Alain

“…Introduction Cladribine Tablets (MAVENCLAD ® ) selectively reduce absolute lymphocyte counts (ALCs) in patients with multiple sclerosis. The recommended…”
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Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data by Jiang, Yulun, García-Durán, Alberto, Losada, Idris Bachali, Girard, Pascal, Terranova, Nadia

“…The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its…”
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Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices by Terranova, Nadia, Renard, Didier, Shahin, Mohamed H., Menon, Sujatha, Cao, Youfang, Hop, Cornelis E.C.A., Hayes, Sean, Madrasi, Kumpal, Stodtmann, Sven, Tensfeldt, Thomas, Vaddady, Pavan, Ellinwood, Nicholas, Lu, James

“…Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains…”
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Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine by Naik, Kunal, Goyal, Rahul K., Foschini, Luca, Chak, Choi Wai, Thielscher, Christian, Zhu, Hao, Lu, James, Lehár, Joseph, Pacanoswki, Michael A., Terranova, Nadia, Mehta, Neha, Korsbo, Niklas, Fakhouri, Tala, Liu, Qi, Gobburu, Jogarao

“…Technological innovations, such as artificial intelligence (AI) and machine learning (ML), have the potential to expedite the goal of precision medicine,…”
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