scholarly journals Integrated Ligand and Structure based approaches towards developing novel Janus Kinase 2 inhibitors for the treatment of myeloproliferative neoplasms

2020 ◽  
Author(s):  
Unni.P Ambili ◽  
Girinath G. Pillai ◽  
Lulu.S Sajitha

AbstractMyeloproliferative neoplasms (MPNs) are a group of diseases affecting hematopoiesis in humans. Types of MPNs include Polycythemia Vera (PV), Essential Thrombocythemia (ET) and myelofibrosis. JAK2 gene mutation at 617th position act as a major causative factor for the onset and progression of MPNs. So, JAK2 inhibitors are widely used for the treatment of MPNs. But, increased incidence of adverse drug reactions associated with JAK2 inhibitors acts as a paramount challenge in the treatment of MPNs. Hence, there exists an urgent need for the identification of novel lead molecules with enhanced potency and bioavailability. We employed ligand and structure-based approaches to identify novel lead molecules which could act as JAK2 inhibitors. The dataset for QSAR modeling (ligand-based approach) comprised of 49 compounds. We have developed a QSAR model, which has got statistical as well as biological significance. Further, all the compounds in the dataset were subjected to molecular docking and bioavailability assessment studies. Derivative compounds with higher potency and bioavailability were identified for the best lead molecule present in the dataset by employing chemical space exploration. Dataset and models are available at https://github.com/giribio/agingdataAbstract FigureGraphical abstract

Author(s):  
Srdan Verstovsek

Overview: The discovery that a somatic point mutation (JAK2V617F) in the Janus kinase 2 ( JAK2) is highly prevalent in patients with myeloproliferative neoplasms (MPNs) has been a crucial breakthrough in our understanding of the underlying molecular mechanisms of these diseases. Therefore, preclinical and clinical research in recent years has focused intensely on the development of new therapies targeted to JAK2. These efforts culminated in recent approval of ruxolitinib as the first official therapy for patients with intermediate- or high-risk myelofibrosis (MF). Therapy with JAK2 inhibitors substantially improves quality of life and reduces organomegaly in MF with or without JAKV617F mutation. Recent results suggest that patients with advanced MF may live longer when receiving therapy with ruxolitinib. However, JAK2 inhibitors do not eliminate the disease and new medications are needed to expand on the benefits seen with JAK2 inhibitors. Although many agents are still in the early stages of development, the wealth of publications and presentations has continued to support our growing understanding of the pathophysiology of MF as well as the potential short- and long-term outcomes of these new and diverse approaches to treatment. Focus of ongoing efforts is particularly on the improvements in anemia and fibrosis, as well as on rational combination trials of JAK2 inhibitors and other potentially active agents. Therapeutic potential and limitations of JAK2 inhibitors and other novel medications in clinical studies are reviewed.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5480-5480
Author(s):  
Richard Wong ◽  
Shulei Sun ◽  
Huan-You Wang ◽  
Helen E. Broome ◽  
Sarah Murray ◽  
...  

Abstract Philadelphia chromosome negative myeloproliferative neoplasms (MPNs) are characterized by the overproduction of mature blood cells and variable bone marrow fibrosis. MPNs attributed to dysregulation of the Janus kinase 2 (JAK2) pathway include polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). Somatic mutations in JAK2, thrombopoietin receptor (MPL), and calreticulin (CALR) have been identified as driver mutations with direct or upstream upregulation of JAK2. CALR mutations are the most recently described, with the two most common mutations being a 52-base pair (bp) deletion (type 1) or 5 bp insertion (type2) in exon 9. Studies have shown a prognostic advantage to type1/type 1 like CALR driven MPNs over JAK2, MPL, and type2 CALR driven MPNs. Rarer CALR exon 9 mutations have also been identified in presumed MPN patients negative for JAK2 and MPL mutations. In these cases variable predicted changes to the CALR protein have resulted in speculative interpretations as to their relevance in the diagnosis of a suspected MPN. Here we report a patient with a longstanding history of myelofibrosis, thrombocytosis, and anemia, eventually determined to have an in-frame presumed germline (due to variant fraction and identification in the patient's child) CALR mutation downstream of a somatic type 1 CALR mutation. The overall compounded alterations generate a type 1 like mutation previously not described (to the best of our knowledge) in the literature. The patient is a 70-year old female noted to be persistently anemic all her life. While a bone marrow assessment was recommended early in life, the patient declined workup until a marrow biopsy was eventually performed at age 50. The biopsy reportedly showed mild marrow fibrosis and the patient was trailed on erythropoietin for her anemia with little benefit. At age 59 the patient was noted with thrombocytosis (478 X 109/L) and mild splenomegaly. Repeat marrow biopsy showed hypercellular marrow, marked fibrosis (WHO grade 3/3), and megakaryocyte clustering. JAK2 was noted to not be mutated. Over the next decade the patient developed symptomatic splenomegly and continued to be anemic with eventual intermittent transfusion requirement at age 65, pushing her risk to DIPPS-plus intermediate-2. During this period successive treatments included Revlimid, trial sonic hedgehog (shh) inhibitor, and JAK2 inhibitors with intervening multiyear long spans without treatment. Marrow fibrosis over this time was predominantly unchanged on the various therapies but symptomatic splenomegaly decreased on shh and JAK2 inhibitors. At present the patient requires intermittent transfusions and is on a JAK2 inhibitor. Recent NGS testing of marrow aspirate identified an in-frame presumed germline CALR mutation downstream of a compound somatic type 1 CALR mutation. A high molecular risk ASXL1 mutation was also identified. The in-frame CALR mutation results in a 9bp in frame deletion (c.1191_1199del, p.398_400delGluGluAsp), which has been reported at least 10 times in the literature. The various reports have one event mentioned as being presumably germline and non-pathogenic, while the other reports are equivocal to presumed pathogenic in light of negative JAK2 and MPL mutations in patients with clinical suspicion for a MPN. At our institution we have identified 4 instances of this 9bp deletion, 3 show allele fractions suggestive of being germline and 1 case with an allele fraction consistent with being a somatic mutation. In one germline case the patient also had a JAK2 V617F mutation and a diagnosis of a MPN. The other presumed germline case was found in an offspring of the patient described in this report and currently shows no signs of a MPN. The presumed somatic 9bp deletion was seen in patient with a hypocellular marrow myelodysplastic syndrome, found to also have a TET2 mutation and normal karyotype. While in-frame CALR exon 9 mutations are rare and predominantly considered germline non-pathogenic polymorphisms, there may be value in reporting such events in the context of patients with myeloid neoplasms as to not miss possible disease modifying mutations which may become apparent when aggregating multi-institution data sets. The patient highlighted in this report exhibits a strikingly long and relatively indolent disease course, notably despite an adverse prognostic risk category and high molecular risk mutation. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2010 ◽  
Vol 115 (25) ◽  
pp. 5232-5240 ◽  
Author(s):  
Jeffrey W. Tyner ◽  
Thomas G. Bumm ◽  
Jutta Deininger ◽  
Lisa Wood ◽  
Karl J. Aichberger ◽  
...  

Abstract Activating alleles of Janus kinase 2 (JAK2) such as JAK2V617F are central to the pathogenesis of myeloproliferative neoplasms (MPN), suggesting that small molecule inhibitors targeting JAK2 may be therapeutically useful. We have identified an aminopyrimidine derivative (CYT387), which inhibits JAK1, JAK2, and tyrosine kinase 2 (TYK2) at low nanomolar concentrations, with few additional targets. Between 0.5 and 1.5μM CYT387 caused growth suppression and apoptosis in JAK2-dependent hematopoietic cell lines, while nonhematopoietic cell lines were unaffected. In a murine MPN model, CYT387 normalized white cell counts, hematocrit, spleen size, and restored physiologic levels of inflammatory cytokines. Despite the hematologic responses and reduction of the JAK2V617F allele burden, JAK2V617F cells persisted and MPN recurred upon cessation of treatment, suggesting that JAK2 inhibitors may be unable to eliminate JAK2V617F cells, consistent with preliminary results from clinical trials of JAK2 inhibitors in myelofibrosis. While the clinical benefit of JAK2 inhibitors may be substantial, not the least due to reduction of inflammatory cytokines and symptomatic improvement, our data add to increasing evidence that kinase inhibitor monotherapy of malignant disease is not curative, suggesting a need for drug combinations to optimally target the malignant cells.


Blood ◽  
2017 ◽  
Vol 130 (2) ◽  
pp. 115-125 ◽  
Author(s):  
Prithviraj Bose ◽  
Srdan Verstovsek

Abstract Since its approval in 2011, the Janus kinase 1/2 (JAK1/2) inhibitor ruxolitinib has evolved to become the centerpiece of therapy for myelofibrosis (MF), and its use in patients with hydroxyurea resistant or intolerant polycythemia vera (PV) is steadily increasing. Several other JAK2 inhibitors have entered clinical testing, but none have been approved and many have been discontinued. Importantly, the activity of these agents is not restricted to patients with JAK2 V617F or exon 12 mutations. Although JAK2 inhibitors provide substantial clinical benefit, their disease-modifying activity is limited, and rational combinations with other targeted agents are needed, particularly in MF, in which survival is short. Many such combinations are being explored, as are other novel agents, some of which could successfully be combined with JAK2 inhibitors in the future. In addition, new JAK2 inhibitors with the potential for less myelosuppression continue to be investigated. Given the proven safety and efficacy of ruxolitinib, it is likely that ruxolitinib-based combinations will be a major way forward in drug development for MF. If approved, less myelosuppressive JAK2 inhibitors such as pacritinib or NS-018 could prove to be very useful additions to the therapeutic armamentarium in MF. In PV, inhibitors of histone deacetylases and human double minute 2 have activity, but their role, if any, in the future treatment algorithm is uncertain, given the availability of ruxolitinib and renewed interest in interferons. Ruxolitinib is in late-phase clinical trials in essential thrombocythemia, in which it could fill an important void for patients with troublesome symptoms.


2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.


Author(s):  
Mahmoud A. Al-Sha'er ◽  
Mutasem O. Taha

Introduction: Tyrosine threonine kinase (TTK1) is a key regulator of chromosome segregation. TTK targeting received recent concern for the enhancement of possible anticancer therapies. Objective: In this regard we employed our well-known method of QSAR-guided selection of best crystallographic pharmacophore(s) to discover considerable binding interactions that anchore inhibitors into TTK1 binding site. Method:Sixtyone TTK1 crystallographic complexes were used to extract 315 pharmacophore hypotheses. QSAR modeling was subsequently used to choose a single crystallographic pharmacophore that when combined with other physicochemical descriptors elucidates bioactivity discrepancy within a list of 55 miscellaneous inhibitors. Results: The best QSAR model was robust and predictive (r2(55) = 0.75, r2LOO = 0.72 , r2press against external testing list of 12 compounds = 0.67), Standard error of estimate (training set) (S)= 0.63 , Standard error of estimate (testing set)(Stest) = 0.62. The resulting pharmacophore and QSAR models were used to scan the National Cancer Institute (NCI) database for new TTK1 inhibitors. Conclusion: Five hits confirmed significant TTK1 inhibitory profiles with IC50 values ranging between 11.7 and 76.6 micM.


2021 ◽  
Vol 15 ◽  
pp. 117793222110303
Author(s):  
Asad Ahmed ◽  
Bhavika Mam ◽  
Ramanathan Sowdhamini

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.


2021 ◽  
Author(s):  
Adarsh Kalikadien ◽  
Evgeny A. Pidko ◽  
Vivek Sinha

<div>Local chemical space exploration of an experimentally synthesized material can be done by making slight structural</div><div>variations of the synthesized material. This generation of many molecular structures with reasonable quality,</div><div>that resemble an existing (chemical) purposeful material, is needed for high-throughput screening purposes in</div><div>material design. Large databases of geometry and chemical properties of transition metal complexes are not</div><div>readily available, although these complexes are widely used in homogeneous catalysis. A Python-based workflow,</div><div>ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule, is introduced.</div><div>The overall computational workflow of ChemSpaX is explained in more detail. ChemSpaX uses 3D information,</div><div>to place functional groups on an input structure. For example, the input structure can be a catalyst for which one</div><div>wants to use high-throughput screening to investigate if the catalytic activity can be improved. The newly placed</div><div>substituents are optimized using a computationally cheap force-field optimization method. After placement of</div><div>new substituents, higher level optimizations using xTB or DFT instead of force-field optimization are also possible</div><div>in the current workflow. In representative applications of ChemSpaX, it is shown that the structures generated by</div><div>ChemSpaX have a reasonable quality for usage in high-throughput screening applications. Representative applications</div><div>of ChemSpaX are shown by investigating various adducts on functionalized Mn-based pincer complexes,</div><div>hydrogenation of Ru-based pincer complexes, functionalization of cobalt porphyrin complexes and functionalization</div><div>of a bipyridyl functionalized cobalt-porphyrin trapped in a M2L4 type cage complex. Descriptors such as</div><div>the Gibbs free energy of reaction and HOMO-LUMO gap, that can be used in data-driven design and discovery</div><div>of catalysts, were selected and studied in more detail for the selected use cases. The relatively fast GFN2-xTB</div><div>method was used to calculate these descriptors and a comparison was done against DFT calculated descriptors.</div><div>ChemSpaX is open-source and aims to bolster the efforts of the scientific community towards data-driven material</div><div>discovery.</div>


Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5574
Author(s):  
Filipe G. A. Estrada ◽  
Silvia Miccoli ◽  
Natália Aniceto ◽  
Alfonso T. Garcia-Sosa ◽  
Rita C. Guedes

Multiple myeloma is an incurable plasma cell neoplastic disease representing about 10–15% of all haematological malignancies diagnosed in developed countries. Proteasome is a key player in multiple myeloma and proteasome inhibitors are the current first-line of treatment. However, these are associated with limited clinical efficacy due to acquired resistance. One of the solutions to overcome this problem is a polypharmacology approach, namely combination therapy and multitargeting drugs. Several polypharmacology avenues are currently being explored. The simultaneous inhibition of EZH2 and Proteasome 20S remains to be investigated, despite the encouraging evidence of therapeutic synergy between the two. Therefore, we sought to bridge this gap by proposing a holistic in silico strategy to find new dual-target inhibitors. First, we assessed the characteristics of both pockets and compared the chemical space of EZH2 and Proteasome 20S inhibitors, to establish the feasibility of dual targeting. This was followed by molecular docking calculations performed on EZH2 and Proteasome 20S inhibitors from ChEMBL 25, from which we derived a predictive model to propose new EZH2 inhibitors among Proteasome 20S compounds, and vice versa, which yielded two dual-inhibitor hits. Complementarily, we built a machine learning QSAR model for each target but realised their application to our data is very limited as each dataset occupies a different region of chemical space. We finally proceeded with molecular dynamics simulations of the two docking hits against the two targets. Overall, we concluded that one of the hit compounds is particularly promising as a dual-inhibitor candidate exhibiting extensive hydrogen bonding with both targets. Furthermore, this work serves as a framework for how to rationally approach a dual-targeting drug discovery project, from the selection of the targets to the prediction of new hit compounds.


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