Pirtobrutinib: First Approval

Keam, Susan J.

doi:10.1007/s40265-023-01860-1

Cited by 15 publications

(11 citation statements)

References 14 publications

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“…Bruton tyrosine kinase (BTK) is involved in B cell development and maturation and plays a role in B cell malignancies such as mantle cell lymphoma (Keam 2023c ). BTK inhibitors are the standard treatment for B cell malignancies (Hatashima et al 2022 ).…”

Section: Oncologymentioning

confidence: 99%

“…The next-in-class small molecule pirtobrutinib is an oral, potent, highly selective, and noncovalent BTK inhibitor regardless of the BTK C481S mutation status (Mato et al 2021 ). Pirtobrutinib was first approved in January 2023 under accelerated approval for treating of relapsed or refractory mantle cell lymphoma in adults following two or more lines of systemic therapy (Keam 2023c ). In December 2023, pirtobrutinib was approved for chronic lymphocytic leukemia and small lymphocytic lymphoma.…”

Section: Oncologymentioning

confidence: 99%

“…Pirtobrutinib received priority review, fast track designation, and orphan drug designation. Low levels of neutrophils, lymphocytes, platelets and hemoglobin, tiredness, pain, bruising, and diarrhea are the most frequent adverse effects (AE) of pirtobrutinib (Keam 2023c ).…”

Section: Oncologymentioning

confidence: 99%

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A year in pharmacology: new drugs approved by the US Food and Drug Administration in 2023

Kayki-Mutlu,

Aksoyalp,

Wojnowski

et al. 2024

Naunyn-Schmiedeberg's Arch Pharmacol

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With 54 new drugs and seven cellular and gene therapy products, the approvals by the US Food and Drug Administration (FDA) recovered 2023 from the 2022 dent back to the levels of 2020–2021. As in previous years of this annual review, we assign these new drugs to one of three levels of innovation: first drug against a condition (“first-in-indication”), first drug using a novel molecular mechanism (“first-in-class”), and “next-in-class,” i.e., a drug using an already exploited molecular mechanism. We identify four (7%) “first-in-indication,” 22 (36%) “first-in-class,” and 35 (57%) “next-in-class” drugs. By treatment area, rare diseases (54%) and cancer drugs (23%) were once again the most prevalent (and partly overlapping) therapeutic areas. Other continuing trends were the use of accelerated regulatory approval pathways and the reliance on biopharmaceuticals (biologics). 2023 marks the approval of a first therapy based on CRISPR/Cas9 gene editing.

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Section: Oncologymentioning

confidence: 99%

Section: Oncologymentioning

confidence: 99%

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A year in pharmacology: new drugs approved by the US Food and Drug Administration in 2023

Kayki-Mutlu,

Aksoyalp,

Wojnowski

et al. 2024

Naunyn-Schmiedeberg's Arch Pharmacol

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“…A fundamental principle underlying targeted therapy is the development of small molecules or monoclonal antibodies that precisely target enzymes or genes responsible for promoting cancer growth. Imatinib’s success paved the way for the creation of numerous cancer therapeutics designed as inhibitors against kinase subclasses, including tyrosine, serine, and threonine kinases. − A compiled database until 29 November 2023 registered 80 FDA-approved protein kinase inhibitors, with pirtobrutinib as the latest FDA-approved BTK inhibitor to cure chronic lymphocytic leukemia or small lymphocytic lymphoma. − From the historical perspective, in 1999, sirolimus was the first small-molecule kinase inhibitor (SMKI) discovered from the natural product. The 2001–2010 period was marked by several pioneering discoveries of small-molecule kinase inhibitors (SMKIs); there were 10 FDA-approved kinase inhibitors (Figure ).…”

Section: Introductionmentioning

confidence: 99%

Structure–Activity Relationship Study and Design Strategies of Hydantoin, Thiazolidinedione, and Rhodanine-Based Kinase Inhibitors: A Two-Decade Review

Naufal,

Hermawati,

Syah

et al. 2024

ACS Omega

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Cancer is one of the most prominent causes of the rapidly growing mortality numbers worldwide. Cancer originates from normal cells that have acquired the capability to alter their molecular, biochemical, and cellular traits. The alteration of cell signaling enzymes, such as kinases, can initiate and amplify cancer progression. As a curative method, the targeted therapy utilized small molecules' capability to inhibit kinase's cellular function. This review provides a brief history (1999−2023) of Small Molecule Kinase Inhibitors (SMKIs) discovery with their molecular perspective. Furthermore, this current review also addresses the application and the development of hydantoin, thiazolidinedione, and rhodanine-based derivatives as kinase inhibitors toward several subclasses (EGFR, PI3K, VEGFR, Pim, c-Met, CDK, IGFR, and ERK) accompanied by their structure−activity relationship study and their molecular interactions. The present work summarizes and compiles all the important structural information essential for developing hydantoin, thiazolidinedione, and rhodanine-based kinase inhibitors to improve their potency in the future.

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“…Currently, there are four BTK inhibitors of the second-generation in the clinical stage, namely Fenebrutinib (Phase III, Biogene) [15], ARQ-531 (Phase I/II, Merck) [16], CG806 (Phase I/II, Aptose Biosciences Inc) [17] and AS-1763 (Phase I, BioNova Pharmaceuticals, Carna Biosciences Inc) [18]. Notably, Pirtobrutinib (LOXO-305) has successfully transitioned from clinical development to market, having received FDA approval (Jaypirca, Eli Lilly and Company) for the treatment of relapsed or refractory mantle cell lymphoma [19].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification Models for Non-Covalent Bruton's Tyrosine Kinase Inhibitors: Predictive Ability and Interpretability

Tian

et al. 2023

Preprint

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In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton's tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. To achieve this, we gathered data on BTK inhibitors from the Reaxys and ChEMBL databases, removing compounds with covalent bonds and duplicates to obtain a dataset of 3895 inhibitors of non-covalent. These inhibitors were characterized using MACCS fingerprints and Morgan fingerprints, and four traditional machine learning algorithms (decision trees (DT), random forests (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost)) were used to build 16 classification models. In addition, four deep learning models were developed using deep neural networks (DNN). The best model, Model D_4, which was built using XGBoost and MACCS fingerprints, achieved an accuracy of 94.1% and a Mathews correlation coefficient (MCC) of 0.75 on the test set. To provide interpretable explanations, we employed the SHAP method to decompose the predicted values into the contributions of each feature. We also used K-means dimensionality reduction and hierarchical clustering to visualize the clustering effects of molecular structures of the inhibitors. The results of this study were validated using crystal structures, and we found that the interaction between the BTK amino acid residue and the important features of clustered scaffold was consistent with the known properties of the complex crystal structures. Overall, our models demonstrated high predictive ability, and a qualitative model can be converted to a quantitative model to some extent by SHAP, making them valuable for guiding the design of new BTK inhibitors with desired activity.

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Pirtobrutinib: First Approval

Cited by 15 publications

References 14 publications

A year in pharmacology: new drugs approved by the US Food and Drug Administration in 2023

A year in pharmacology: new drugs approved by the US Food and Drug Administration in 2023

Structure–Activity Relationship Study and Design Strategies of Hydantoin, Thiazolidinedione, and Rhodanine-Based Kinase Inhibitors: A Two-Decade Review

Machine Learning-Based Classification Models for Non-Covalent Bruton's Tyrosine Kinase Inhibitors: Predictive Ability and Interpretability

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