Tailoring Liver Pyruvate Kinase Inhibitors via Structural Guided Optimization

1. Tailoring Liver Pyruvate Kinase
Inhibitors via Structural Guided
Optimization
Dr. Sajda Ashraf

2. Introduction
Molecular modelling
Target selection
Synthesis and invitro activities
Conclusion
Presentation Plan

3. North America
24.1%
Europe
23.7%
Europe
23.7% Asia
27.4%
Africa
13.5%
Middle East
31.8%
South America
30.5%

4. Metabolic associated fatty
liver disease (MAFLD)

5. 5
The Problem – NAFLD
 NAFLD, metabolic disease, and its advanced form may transform to
liver cirrhosis.
 There is no FDA approved drug that could be used to treat NAFLD
patient effectively.
 Prevalence of NAFLD is approximately 25% of the population, no
approved effective pharmacological treatment.
 Kinases selectivity

6. 6
 538 protein kinase genes
 20% of all eukaryotic genes
 218 genes cause human diseases
 Approx. 100 dominant oncogenes

7. Protein Kinases Involved in Diabetes Mellitus
7
Protein Kinases Involved in Cancer

9. Pathway of JNK
https://www.sciencedirect.com/science/article/pii/S1567576922010724

10. MAPK8 TP53 USF1
The core mechanism of JNK-5A
PKLR

11. Selection of
protein target
•Binding site
Characterization
•Isoform selectivity
Enumeration
•Enumerating with an
R-Group Library
•Prediction of
physicochemical
properties
Docking of
enumerated
compounds
Synthesis of
selected
compounds
Invitro validation
Work Plan

12. Superposition of JNK isoforms
JNK3 (MAPK10)
JNK1 (MAPK8)

13. Docking Score: -7.78321 Docking Score: -4.99666
Docked Conformation of JNK-5A
JNK-3 JNK-1

14. Modifications for PKL Inhibitor
Possible modification sites
3D interaction of JNK-5A
14

15. Electrostatic Interactions
• Lys93, Met146, Met149
Hydrophobic Interactions
• Val78, Ala91, Lys93, Met115, ILe124, Leu126, Leu144, Met146,
Leu206
Crucial Residues of JNK3 active site

19. Enumeration

20. Enumeration

21. Compounds Molecular structures
Computational
algorithms Molecular descriptors
JNK Inhibitor IX
Properties
Descriptor Calculation

22. S.No. Compounds Docking Scores/JNK-3 Docking Scores/JNK-1
7 PKL-A59 -8.06717 -5.95493
16 PKL-A69 -7.85095 -7.12977
19 PKL-A72 -8.14933 -7.73003
21 PKL-A74 -9.29021 -6.74792
23 PKL-A76 -8.20401 -7.65688
24 JNK-5A -7.78321 -4.99666
37 PKL-A13 -8.29558 -4.12134
38 PKL-A14 -7.88422 -5.51766
39 PKL-A15 -8.26142 -3.29845
51 PKL-A27 -7.7964 -8.14012
53 PKL-A29 -8.05147 -6.87756
55 PKL-A31 -7.82245 -5.33177
56 PKL-A32 -7.98573 -5.82365
59 PKL-A35 -7.92778 -4.54927
61 PKL-A37 -7.79931 -4.82226
62 PKL-A38 -9.29014 -7.87622
63 PKL-A39 -7.88229 -6.78379
81 B5.mol2 -8.84674 -8.11743
82 B6.mol2 -8.88167 -6.42115
83 B7.mol2 -8.3955 -6.7511
84 B8.mol2 -8.18234 -7.74687
85 B9.mol2 -8.31269 -8.26773
86 B10.mol2 -8.17018 -8.33157
95 B19.mol2 -8.79186 -4.05543
111 B35 -8.2978 -5.20407
118 B42 -8.39687 -3.61454
126 2O2U.A -8.61211 -6.89722
127 PKL-A77 -8.29551 -5.64757
Docking Scores of JNK Isoforms

23. Alignment of JNK Analogues

24. Synthesis of JNK
analogues
Dr. Shazia Iqbal

25. 0
20
40
60
80
100
120
Control
A159
A158
A152
A151
A162
A119
A163
A117
A157
A132
A131
A161
A153
A156
A154
A114
A160
B130
A164
B171
B172
A192
A193
A190
B133
A149
JNK
B153
B173
A179
B129
A56
B164
A183
A135
B170
A118
Cell
viability
(%)
TAG
MTT
Biological validation of insilico results
The rankings of the most active compounds in HepG2 cell line. The effects of the hit compounds on triacylglyceride (TAG)
levels and cell viability in 7-day steatosis cell model.

26. Selectivity Profile
Phospho-C-Jun
JNK1
JNK2
JNK3
P-C-Jun
P-C-Jun
P-C-Jun
Purified JNK1, JNK2, JNK3 had tested with 4 top derivatives.
Drugs showed inhibition JNK3 mostly.
FASN
PKL
ACACA
ChREBP
PKM2
Actin
SCD1
STAT1
JNK3
1week sts with JNK hit compound top6

27. -Modification to sulfone
led to loss of activity
-Modifications in position 4
with donor group is tolerated
-Heterocyclic modifications
led to inactive analogues
- Modifications led to
loss of activity
-Cycloheptane ring,
urea substitued
piperdine rings led
to potent inhibitors
Structural insights of JNK analogues

28. Conclusion
Overcoming Challenges in Developing Effective Liver Pyruvate Kinase Inhibitors through Structural
Guided Optimization:
 Structural complexity
 Off-target effect
 Safety and Toxicity

30. Dr. Sajda Ashraf
Email: sajda.ashraf@trustlifelabs.com
LinkedIn: https://www.linkedin.com/in/sajda-ashraf-90920a48
Website: https://www.trustlifeventures.com