An emerging field of R & D

1. QSP
Quantitative and Systems Pharmacology in Drug Discovery
and development
Presenter -DrMathanKumar
Moderator-DrAnandSrinivasan

2. Overview
• Current process of Drug
Discovery and its limitations
• QSP – concepts & workflow
• QSP’s Impact & significance
• Challenges & controversies
• Current & future prospects
• Some case studies

3. Present paradigm of Drug discovery

4. 90% of clinical drug development fails
SunD,GaoW,Hu H,ZhouS.Why90%ofclinicaldrugdevelopment failsandhowtoimproveit?ActaPharmSinB.2022Jul;12(7):3049-3062.doi:10.1016/j.apsb.2022.02.002.Epub 2022Feb11.PMID:35865092;PMCID:PMC9293739.

5. What is the reason for failure
Not addressingthephysiology of whole system.
Not addressingfor variabilities(such as variability indrug responseat
different cellular-tissue-organism level)
Targeting wrong
• Patientsub-population, mechanismpathway
• Sub-optimal dosing
Lackof complete understandingof MOA ofmanydrugs.
Wrong preclinical model used.Hence,poor transition frompreclinical
datato clinical settings

6. Advancesinthe
PK,
PKPD,
PBPK modelling,
What has changed in a decade ?

7. Current era
Caninclude
thisinR&D?
Artificial
intelligence
Computational
advancements
Availabilityof
bigdata,
Electronic
Healthrecords
Emerging
conceptsof
personalized
medicine

9. Fundamental Pillars of Pharmacology
• Talksaboutdownstream
function(effect)
• EC50
• Durationofaction
• TalksaboutF,Vd,CL
(clearance),
• Doseandconcentrationat
targetsite
QSP
Q
S
P

10. QSP
Pharmacology
PK PD
Systems
Biology
Physiological
regulation
Pathological
modifications
Pathway
modulation

11. QSP
Emergingdisciplineintranslationalmedicine
Innovativeinchanging“one-gene, one-receptor,one-
mechanism”approachtoasystemslevelnetworkapproach
inunderstandingofdiversemechanisms
Combinescomputationalandexperimentalmethodsto
understandhowdrugsmodulatecellularnetworks&
impacthumanpathophysiologybyusingmathematical
models(ODE)

12. Through QSP we can,
• Besttargets&biomarkersforagivendisease
Find
• Therapeuticeffectivenessofanexistingdrugthroughcombination
therapy
Improve
• Humanresponsedosebasedonpreclinicaldatafornewdrug
• Effectof adruginaspecialpopulation/otherindication
Predict
• Dosingregimenbasedonpatientcharacteristics
Individualize
• Clinicaltrialsbyaccountingfordruginteractions&genotypes.
Optimize
• Pastfailuresindrugdiscovery
Understand

13. In Comparison to
other modelling

14. How is it done ?
• From a traditionalfield-specific approach to interdisciplinary approach.

15. approaches for QSP
Statistical (Bayesian)
Differential equationsbased
ODE- Temporal
PDE- Spatio-temporal
Boolean, logical
Machinelearning

16. Tools for QSP
• R-based packages
• nlmixr,
• mrgsolve,
• RxODE,
• nlme
• MATLAB
• SimBiology

17. Workflow for QSP
Interaction between collaborators to identify the project goals and collection
of data for relevant biological variables
Development of appropriate family of ODEs from the accumulated knowledge
in biology
Numerical analysis of the ODE
Model generation
Validation if experimental data available or conduct experiment to validate the
model
Final model
Successful
Validation
Validation
unsuccessful

18. Virtual Insilco Clinical trial

19. What will
be the
impact ?
Significantimpactonmultiplestagesofthedrugdiscovery
pipeline
Complementgenomicmedicineasitservesasalinkbetween
pharmacologywithsystems-level “omics”data.
Understandingtheoriginsofpatient-to-patientvariabilityin
therapeuticresponse/adverseevents.
Translationofpreclinicaldiscoveriesintoclinicalsuccess,
particularlywithrespecttotheefficacyof PhaseIIclinicaltrials.

20. Challenges and controversies
• Require the participation of investigators with different skillsets
• Interdisciplinary innovation is needed
• Cooperation of industry (which has much of the necessary data) &
academics (who have the time and expertise to analyze the data).Hence
"Industrial grade" software engineering talent is needed.
Systems biologists
• QuantitativeandcausalRelationship
Pharmacologists
• Focus on pharmacotherapyinthe clinical
relationships

21. Current and future prospects

22. Examples of some QSP models

23. A case study of Parkinsonism

24. QSP model- S. Bakshi, PH. Vander Graaf (Breda, Netherlands)
Toaddresspastfailuresindrugdiscovery
S.Bakshi,PH. VanderGraaf.QuantitativesystemspharmacologymodellingofParkinson’sdiseasetocompareanti-α-synucleintherapeutics[abstract].MovDisord.2021;36(suppl1).
https://www.mdsabstracts.org/abstract/quantitative-systems-pharmacology-modelling-of-parkinsons-disease-to-compare-anti-%ce%b1-synuclein-therapeutics/.AccessedOctober14,2022.

27. Virtual twin for
missingness

28. QSP model for Dose optimization

29. Predicting efficacy

30. Predicting efficacy

31. Drug combination
therapy

32. understand MOA

33. Take home points
• QSP isanewemergingapproachindrugdiscovery
• Itinvolves collaborationbetween variousexperts
• Usingmathematicalmodel forbiologicalvariables ,predictthefutureresponse to
adrugforthespecifictarget.
• VirtualInsilcoclinicaltrail
• Futurescope inOncology, immunology, neurology