scholarly journals AB0377 COMPUTATIONAL DISCOVERY AND PRECLINICAL VALIDATION OF THERAPEUTIC LEADS WITH NOVEL MOAS FOR SYSTEMIC LUPUS ERYTHEMATOSUS (SLE)

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1489.2-1489
Author(s):  
I. Hakim ◽  
S. Mujahid ◽  
A. C. Daugherty ◽  
T. S. Heuer

Background:Lupus is a heterogeneous, systemic disease that affects millions of patients globally with a high unmet medical need. We present results from our powerful and efficient computational drug discovery platform that identifies hits with first-in-class mechanisms of action that can advance rapidly and successfully through preclinical validation studies. The twoXAR discovery platform uses an artificial-intelligence framework to integrate diverse patient-derived biomedical data sets to build holistic and unbiased models of human disease biology. The utilization of diverse, proprietary algorithms and deep learning principles provides a highly sensitive platform to elucidate complex disease-specific associations between biology and biomedical data that are integrated with a library of existing drug molecules. This enables the identification of novel, high-value drug discovery hits with known pharmacological properties. The twoXAR platform also preserves interpretable data-driven links to disease biology to facilitate efficient validation and optimization studies.Objectives:Apply twoXAR’s computational drug discovery platform for the discovery of first-in-class lupus therapy hits and perform preclinical characterization of selected hits to identify drug discovery lead molecules.Methods:Using clinical SLE patient data, we employed the twoXAR platform to build anin-silicoSLE disease model. Nine molecules with novel mechanisms of action (not previously tested as candidate clinical therapies for lupus) were identified as drug discovery hits and then characterized in preclinical efficacy studies using the MRL mouse model of lupus.Results:In preclinical validation studies with the MRL mouse model, 2 compounds were differentiated by significant efficacy and excellent tolerability. TXR-711 and TXR-712 increased renal function, decreased renal inflammation and decreased inflammation compared to vehicle-treated control mice. In particular, TXR-711 and TXR-712 significantly decreased serum blood urea nitrogen (BUN) levels, decreased proteinuria levels, and significantly improved kidney histology readouts such as glomerulonephritis and tubule basophilia. Additionally, TXR-711 and TXR-712 treatment resulted in significantly decreased inguinal lymph node weight.Conclusion:TXR-711 and TXR-712 were identified as SLE drug discovery leads with novel MOAs for further preclinical development. Ongoing studies with TXR-711 and TXR-712 includes pharmacokinetic, pharmacodynamic, and additional MRL mouse efficacy characterization.Disclosure of Interests:Isaac Hakim Employee of: twoXAR, Inc, Sana Mujahid Employee of: twoXAR, Inc., Aaron C. Daugherty Employee of: twoXAR, Inc., Timothy S. Heuer Employee of: twoXAR, Inc

Author(s):  
A. Jainul Fathima ◽  
G. Murugaboopathi

Drug discovery is related to analytics as the method requires a technique to handle the extremely large volume of structured and unstructured biomedical data of multi-dimensional and complexity from pharmaceutical companies. To tackle the complexity of data and to get better insight into the data, big data analytics can be used to integrate the massive amount of pharmaceutical data and computational tools in an analytic framework. This paper presents an overview of big data analytics in the field of drug discovery and outlines an analytic framework which can be applied to computational drug discovery process and briefly discuss the challenges. Hence, big data analytics may contribute to better drug discovery.  


Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1015
Author(s):  
Utsa Bhaduri ◽  
Giuseppe Merla

Ubiquitination is a post-translational modification that has pivotal roles in protein degradation and diversified cellular processes, and for more than two decades it has been a subject of interest in the biotech or biopharmaceutical industry. Tripartite motif (TRIM) family proteins are known to have proven E3 ubiquitin ligase activities and are involved in a multitude of cellular and physiological events and pathophysiological conditions ranging from cancers to rare genetic disorders. Although in recent years many kinds of E3 ubiquitin ligases have emerged as the preferred choices of big pharma and biotech startups in the context of protein degradation and disease biology, from a surface overview it appears that TRIM E3 ubiquitin ligases are not very well recognized yet in the realm of drug discovery. This article will review some of the blockbuster scientific discoveries and technological innovations from the world of ubiquitination and E3 ubiquitin ligases that have impacted the biopharma community, from biotech colossuses to startups, and will attempt to evaluate the future of TRIM family proteins in the province of E3 ubiquitin ligase-based drug discovery.


Pharmaceutics ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 597
Author(s):  
Artur Świerczek ◽  
Hanna Plutecka ◽  
Marietta Ślusarczyk ◽  
Grażyna Chłoń-Rzepa ◽  
Elżbieta Wyska

This study aimed to assess the efficacy and explore the mechanisms of action of a potent phosphodiesterase (PDE)7A and a moderate PDE4B inhibitor GRMS-55 in a mouse model of autoimmune hepatitis (AIH). The concentrations of GRMS-55 and relevant biomarkers were measured in the serum of BALB/c mice with concanavalin A (ConA)-induced hepatitis administered with GRMS-55 at two dose levels. A semi-mechanistic PK/PD/disease progression model describing the time courses of measured biomarkers was developed. The emetogenicity as a potential side effect of the studied compound was evaluated in the α2-adrenoceptor agonist-induced anesthesia model. The results indicate that liver damage observed in mice challenged with ConA was mainly mediated by TNF-α and IFN-γ. GRMS-55 decreased the levels of pro-inflammatory mediators and the transaminase activities in the serum of mice with AIH. The anti-inflammatory properties of GRMS-55, resulting mainly from PDE7A inhibition, led to a high hepatoprotective activity in mice with AIH, which was mediated by an inhibition of pro-inflammatory signaling. GRMS-55 did not induce the emetic-like behavior. The developed PK/PD/disease progression model may be used in future studies to assess the potency and explore the mechanisms of action of new investigational compounds for the treatment of AIH.


2015 ◽  
Vol 20 (11) ◽  
pp. 1328-1336 ◽  
Author(s):  
Chih-Yuan Tseng ◽  
Jack Tuszynski

Autoimmunity ◽  
1994 ◽  
Vol 17 (2) ◽  
pp. 157-163 ◽  
Author(s):  
John S Axford ◽  
Azita Alavi ◽  
Angela Bond ◽  
Frank C Hay

2020 ◽  
Vol 26 (42) ◽  
pp. 7598-7622 ◽  
Author(s):  
Xiao Hu ◽  
Irene Maffucci ◽  
Alessandro Contini

Background: The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions. Objective: In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered. Results: Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules. Conclusions: Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.


Author(s):  
Takahiro Kataoka ◽  
Shota Naoe ◽  
Kaito Murakami ◽  
Ryohei Yukimine ◽  
Yuki Fujimoto ◽  
...  

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