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      Angiopoietin-like 3-derivative LNA043 for cartilage regeneration in osteoarthritis: a randomized phase 1 trial

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          Abstract

          Osteoarthritis (OA) is a common, debilitating, chronic disease with no disease-modifying drug approved to date. We discovered LNA043—a derivative of angiopoietin-like 3 (ANGPTL3)—as a potent chondrogenesis inducer using a phenotypic screen with human mesenchymal stem cells. We show that LNA043 promotes chondrogenesis and cartilage matrix synthesis in vitro and regenerates hyaline articular cartilage in preclinical OA and cartilage injury models in vivo. LNA043 exerts at least part of these effects through binding to the fibronectin receptor, integrin α 5β 1 on mesenchymal stem cells and chondrocytes. In a first-in-human (phase 1), randomized, double-blinded, placebo-controlled, single ascending dose, single-center trial ( NCT02491281; sponsored by Novartis Pharmaceuticals), 28 patients with knee OA were injected intra-articularly with LNA043 or placebo (3:1 ratio) either 2 h, 7 d or 21 d before total knee replacement. LNA043 met its primary safety endpoint and showed short serum pharmacokinetics, cartilage penetration and a lack of immunogenicity (secondary endpoints). Post-hoc transcriptomics profiling of cartilage revealed that a single LNA043 injection reverses the OA transcriptome signature over at least 21 d, inducing the expression of hyaline cartilage matrix components and anabolic signaling pathways, while suppressing mediators of OA progression. LNA043 is a novel disease-modifying OA drug candidate that is currently in a phase 2b trial ( NCT04864392) in patients with knee OA.

          Abstract

          The mechanism of action of LNA043—a novel disease-modifying osteoarthritis treatment candidate—is characterized in preclinical studies and through transcriptomic profiling of cartilage from patients with osteoarthritis in a phase 1 trial.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                nicole.gerwin@novartis.com
                Journal
                Nat Med
                Nat Med
                Nature Medicine
                Nature Publishing Group US (New York )
                1078-8956
                1546-170X
                1 December 2022
                1 December 2022
                2022
                : 28
                : 12
                : 2633-2645
                Affiliations
                [1 ]GRID grid.419481.1, ISNI 0000 0001 1515 9979, Novartis Institutes for BioMedical Research, ; Basel, Switzerland
                [2 ]GRID grid.419481.1, ISNI 0000 0001 1515 9979, Novartis Pharma, ; Basel, Switzerland
                [3 ]GRID grid.418424.f, ISNI 0000 0004 0439 2056, Novartis Institutes for BioMedical Research, ; San Diego, CA USA
                [4 ]GRID grid.418424.f, ISNI 0000 0004 0439 2056, Novartis Institutes for BioMedical Research, ; Cambridge, MA USA
                [5 ]GRID grid.423305.3, ISNI 0000 0004 4902 4281, Division of Scripps Research, , Calibr, ; La Jolla, CA USA
                [6 ]Praxisklinik Rennbahn, Muttenz, Switzerland
                [7 ]Ultivue, Cambridge, MA USA
                [8 ]GRID grid.497480.6, IQVIA, ; Thane, India
                [9 ]GRID grid.512039.c, Arizona Research Center, ; Phoenix, AZ USA
                [10 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Institute for Translational Medicine, , ETH Zürich, ; Zürich, Switzerland
                Author information
                http://orcid.org/0000-0002-4724-1595
                http://orcid.org/0000-0002-4507-1334
                http://orcid.org/0000-0003-0012-0494
                http://orcid.org/0000-0002-3959-3179
                Article
                2059
                10.1038/s41591-022-02059-9
                9800282
                36456835
                b1251d8b-8d95-4558-ab9d-5036517f497f
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 December 2021
                : 28 September 2022
                Funding
                Funded by: Novartis Pharmaceuticals Corporation
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

                Medicine
                osteoarthritis,preclinical research
                Medicine
                osteoarthritis, preclinical research

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