Drug development and Pharmaceutical Research.pdf

Drug development and Pharmaceutical Research

1. Professor & Head, Department of Pharmaceutics
St. Peter’s Institute of Pharmaceutical Sciences,
Hanumakonda, Telangana, India.
Drug development and
Pharmaceutical Research
1. Basic Vs Applied Research and
Drug Development
UGC-sponsored Two -Week Online Refresher Course on
“Drug Discovery and Formulation Development-Interpretation of Research Data”
UGC –MALAVIYA MISSION TEACHER TRAINING CENTRE and JNTU, Hyderabad

2. INTRODUCTION
1
Research:Studiousinquiryorexamination;especially:investigationorexperimentationaimedat
thediscoveryandinterpretationoffacts,revisionofacceptedtheoriesorlawsinthelightofnew
facts,orpracticalapplicationofsuchneworrevisedtheoriesorlaws
Drugdevelopment:Drugdevelopmentreferstotheprocessofbringinganewdrugtothemarket,
involvingstagessuchasleadcompounddiscovery,preclinicalresearch,clinicaltrials,andregulatory
approval.
Artificialintelligence:AIisafieldofsciencethatstudieshowtocreatemachinesandcomputers
thatcanlearn,reason,andactinwaysthatwouldnormallyrequirehumanintelligence.
AIsystemscanuselargeamountsofdatatolearnhowtorecognizepatterns,solveproblems,and
predictfutureevents.
What ?

3. Types of Research Methods
2
Applied or
Advanced
Research
Fundamental
or Basic
Research
Analytical
Research
Descriptive
Research
Quantitative
Research
Qualitative
Research
Empirical
Research
Conceptual
Research
What ?

4. Basic Research Vs Applied Research
3
Expands current knowledge (Hypothetical, theoretical and
exploratory)
Aims to solve problem at hand (Practical and descriptive)
Studies any problem (Wider Scope) Studies problems with important social consequences (Specific Scope)
Tries to say why things happen (Curiosity driven) Tries to say how things can be changed (Client Driven)
Seeks generalisation (Predicts future Phenomena) Individual cases are studied without generalisation
Looks for basic process (Less associated with technology) Looks for any variable making desired difference (Associated with
advancement of technology)
Reports in technical language Reports in common language
Universal but is performed in a limited space (Laboratory) Restricted and guided in an open environment to address a real-world
issue (End utilization).
Interested in expanding scientific understanding and forecasts Innovation for industrial usage, growth of new instruments, and
technological advancement
Concentrated on expanding the current body of evidence and
providing fresh insights into preexisting notions.
Creation of a novel, targeted method to address business and industrial
issues (Has Direct Commercial Objective –Customer Driven)

5. Classification of Applied research
ActionResearch
▸Actionresearchcombinesdatainvestigationwiththeinterpretationofthefindings.Theprimarypurposeof
actionresearchistodiscoverproblemsthatmaybeusedinfutureinvestigations.
EvaluationResearch
▸Evaluationresearchisconcernedwiththeallocationoftime,money,effort,andresourcestoacertainproblemor
cause.Thisstudyapproachisfrequentlyusedbybusinessestoassesshoweffectivelytheyoperate.
ResearchandDevelopment
▸Thissortofstudyattemptstofindnewproductsandservicesbasedonconsumerdemands.Companiesmayalso
utilizethisresearchapproachtouncovermethodstoenhancetheirpresentproductsorservicestobetterfulfill
thedemandsoftheirconsumers.
4

6. Sequentialprocess of PharmaceuticalR&D
1.ResearchandDevelopment:Determiningatargetandcreatingapossiblemedication.
2.PreclinicalResearch:Laboratorytestingtoascertainwhetherhumantestingissafe.
3.ClinicalResearch:Performinghumanstudiestoevaluateefficacyandsafety.
4.FDAReview:SendinginformationforapprovaltotheFDA.
5.Post-MarketingSurveillance:Continuousevaluationstoguaranteesustainedsafety.
5

7. Drug Discovery and development
6
How?
Mohs RC, Greig NH. Drug discovery and development: Role of basic biological research. AlzheimersDement (N Y). 2017 Nov 11;3(4):651-657.

8. Lipinski's rule of five, also known as Pfizer's rule of
7a
The rule predicts high probability of success or failure due to drug likeness for molecules complying
with 2 or more of the following rules:
No more than hydrogen bond donors (nitrogen or oxygen atoms with one or more hydrogen
atoms).
No more than hydrogen bond acceptors (nitrogen or oxygen atoms).
A molecular mass less than Daltons.
An octanol-water partition coefficient (log P) that does not exceed .5

9. Drug likeness
7c
▸Molecularweight
Drugmolecularweightisinverselyproportionaltodrugpermeability
▸LipophilicityLogP
DrugLipophilicityisdirectlyproportionaltodrugpermeability
▸Numberofhydrogenbonddonorsandacceptors
Hydrogenbonddonorsandacceptorsinverselyproportionaltodrugpermeability

10. 8a
Drug development and Pharmaceutical Research
ANewBiologicalEntity(NBE;anantibody,protein,genetherapy,orotherbiologicalmedication)orNewMolecularEntity(NME;asmall
moleculardrug)mustbedevelopedatacostofatleast$1billion,withanaverageestimateofroughly$2.6billion.
Target
to Hit
Hit to leadLead
Optimisation
Non ClinicalPhase1 Phase2 Phase3Submission
to Launch
# per
Launch
24.3 19.4 14.6 12.4 8.6 4.6 1.6 1.1
P (TS) 80% 75% 85% 69% 54% 34% 70% 91%
Cycle time
(yrs)
1.0 1.5 2.0 1.0 1.5 2.5 2.5 1.5
Cost/
Launch
($mil)
$ 94 $ 166 $ 414 $ 150 $ 273 $ 319 $ 314 $ 48

11. R&D Spending
R&Dspendinginthepharmaceuticalindustrycoversfollowingactivities
▸Invention,orresearchanddiscoveryofnewdrugs;
▸Development,orclinicaltesting,preparationandsubmissionofapplicationsforFDA
approval,anddesignofproductionprocessesfornewdrugs;
▸Incrementalinnovation,includingthedevelopmentofnewdosagesanddelivery
mechanismsforexistingdrugsandthetestingofthosedrugsforadditionalindications;
▸Productdifferentiation,ortheclinicaltestingofanewdrugagainstanexistingrivaldrug
toshowthatthenewdrugissuperior;and
▸Safetymonitoring,orclinicaltrials(conductedafteradrughasreachedthemarket)that
theFDAmayrequiretodetectsideeffectsthatmaynothavebeenobservedinshorter
trialswhenthedrugwasindevelopment.
8b

12. Factors Influence Spending for R&D?
▸Anticipated lifetime global revenues from a new drug,
▸Expected costs to develop a new drug, and
▸Policies and programs that influence the supply of and demand for prescription drugs.
8c

13. 9
Drug development and Information to be developed for a
potential clinical candidate molecule
1. Information to be developed for a prospective clinical candidate molecule.
✓Clarity on target validation in regard to human disease.
✓Target’s physiology affected by disease
✓Effective in animal model or disease –What evidence supports relevance to human disease?
2. Reliability of findings –Across multiple essays laboratories doses populations and conditions as
appropriate
3. Specificity of molecule against target
4. Kinetics of molecules
5. Potency of molecules
6. Safety margin in at least two species

14. Individual laboratory contributions to drug discovery and development
10
Target identification -New receptor, enzyme, pathway, protein etc
Target validation -Data linking target to human disease
Finding new molecule (Chemical or biological)
Screening essays -Cell lines, animal models etc
Dataon drug like characteristics pharmacokinetics and toxicology
Developmental tools pharmacodynamic biomarkers including Biochemical essays, PET Ligands (radiotracers used
in positron emission tomography),Electrophysiological measures and others
Efficacy measures -Clinical scales, cognitive tests, functional measures, self reported outcomes, electronic health
recording
Technologies to improve efficiency of trial completion –Recruiting technologies electronic data capture and
tracking, Trial simulation, Safety monitoring etc

15. Challenges in R&D
1.HighCostsandInvestmentRisks:PharmaR&Disexpensiveandcomeswithhighfinancialrisks.
2.RegulatoryHurdles:Navigatingcomplexregulatoryrequirementsisasignificantchallenge.
3.ScientificandTechnicalChallenges:Overcomingscientificbarriersindrugdevelopment.
4.EthicalConsiderations:EnsuringethicalstandardsaremaintainedthroughouttheR&Dprocess.
5.TimeConsumption:Theprocessfromdiscoverytomarketcantakeoveradecade.
11

16. Stages in pharmaceutical R&D
12
▸1.DiscoveryandDevelopment:Identifyingatargetanddevelopingapotentialdrug.
❖Researchersutilizetechniqueslikevirtualscreeningandcompoundscreeningassaystopinpointpromising
compoundsthatinteractpositivelywiththetargetmolecule.
❖Theseinitialcandidatesproceedthroughahitdiscoveryprocesstoidentifyleadcompoundswiththerapeutic
potential.
▸2.PreclinicalResearch:Testinginalabsettingtodetermineifit’ssafetotestleadcompoundsinhumans.
Thisphaseincludes:
▸AnimalTesting
▸Inthepreclinicalphase,selectedleadcompoundsundergoextensivetestinginanimalmodelstoassesstheir
safety,efficacy,andtherapeuticbenefits.Thisstageiscriticalfordeterminingthemaximumtolerateddoseand
establishingapreliminarysafetyprofile.Onlyasmallnumberofcompoundstypicallyadvancepastthisstage
duetotherigorousnatureofthesetests.

17. 13
3. Clinical Research: Conducting trials in humans to test for new drug safety and
effectiveness.
▸PhaseITrials
▸Theclinicaldevelopmentphasekicksoffwithhumantrials.PhaseItrialsfocus
onevaluatingthedrug’spharmacokineticsandpharmacodynamics,aswellas
itssafetyprofileinasmallgroupofhealthyvolunteers.
▸Theydeterminethebasicproperties,includingabsorption,distribution,
metabolism,andexcretion.Therefore,thisphaseispivotalindetermininginitial
safetyanddosageparameters.
Stages in pharmaceutical R&D Contd……

18. ▸PhaseIITrials
▸Movingforward,phaseIItrialsassessthedrug’smedicationeffectivenessandfurtherevaluateitssafetyin
alargerpatientpopulation.
▸Theseclinicalstudieshelpidentifytheoptimaldosingregimenandassessanyshort-termadverse
effects.
▸EvidenceofExposureanditsrelationtodose,frequencyofadministration,patientcharacteristics
(Weight,OrganFunction,etc)
▸EvidenceofTargetengagementanditsrelationshiptodrugPK.DonethroughPET,Biochemicalassays
etc(Targetbindingandmodeofaction).
▸EvidenceofTargetrelatedpharmacodynamiceffectanditsrelationshiptodoseanddrug
pharmacokinetics.
▸SettingofdoseanddosageregimenforphaseIIItrials.
14
Stages in pharmaceutical R&D Contd……

19. PhaseIIITrials
▸PhaseIIItrialsinvolvelarge-scaletestingtogathercomprehensivedataon
thedrug’sefficacy,safety,andoveralltherapeuticbenefit.
▸Thisphasetypicallyincludesthousandsofpatientsacrossvarious
demographicsandlocations,providingrobustdatafordrugapprovaland
marketauthorization.
15
Stages in pharmaceutical R&D Contd……

20. 4.RegulatoryReview(FDAReview:SubmittingdatatotheFDAfor
approval)
▸UponcompletingphaseIIIclinicaltrials,adetaileddrugapplicationissubmittedtoregulatory
authoritiessuchastheFDAorEMAforapproval.Thismeticulousreviewprocessensuresthatthedrug
meetsstringentsafetyandefficacystandardsbeforeitcanbemarketed.
FDAApproval
▸TheFDAapprovalprocessinvolvesanexhaustiveexaminationoftheclinicaldatatoensurethedrug’ssafety
andefficacyforitsintendeduse.
OtherRegulatoryAgencies
▸InadditiontotheFDA,othersignificantregulatoryagenciesincludetheEuropeanMedicinesAgency(EMA),
whichoverseethedrugapprovalprocessintheirrespectiveregions,ensuringglobalcomplianceandsafety
standards.
16
Stages in pharmaceutical R&D Contd……

21. 5.Post-MarketDrugSafetyMonitoring(Post-MarketSurveillance):
Ongoingcheckstoensurelong-termsafety
PhaseIVStudies
▸Post-marketingtrials,knownasphaseIVstudies,continuetomonitorthelong-termsafety
andefficacyofapproveddrugs.
▸Thesestudiesarecrucialforidentifyingrareorlong-termadverseeffectsthatmaynothave
beenapparentduringearliertrials.
▸Theyalsohelpinrefiningthedrug’susageguidelinesandoptimizingtherapeuticbenefits.
17
Stages in pharmaceutical R&D Contd……

22. ▸"Drugdevelopment"isthetermusedtodescribetheentireprocessofintroducinganewdrugor
technologytothemarket.
▸Drugdevelopment,chemistryandpharmacology,nonclinicalsafetytesting,manufacturing,clinical
trials,andregulatorysubmissionsareallpartofthisextensive,multidisciplinaryendeavor.
▸Drugdevelopmentisamulti-phase,intricateprocessthatturnsscientificdiscoveriesinto
therapiesthatcansavelives.
▸Fromtheinitialphasesofdrugresearchtoclinicaltrialsandregulatoryapproval,eachstepis
criticaltothesuccessofnewmedications.
▸Thisintricateprocessrequirescollaboration,stringenttesting,andunwaveringadherenceto
regulatoryrequirementsinordertobringsafeandeffectivepharmaceuticalstomarket.
18
Conclusion

23. Drug development and Pharmaceutical Research
2. Artificial Intelligence and Drug Development
UGC-sponsored Two -Week Online Refresher Course on
“Drug Discovery and Formulation Development-Interpretation of Research Data”

24. Introduction
▸Artificialintelligence(AI)isutilizedinnumerouswaystoimprovedrugresearchanddevelopment,suchas
PredictingDrugProperties
▸AIalgorithmscananticipateadrug'sphysicochemicalqualities,includingsolubility,bioavailability,andtoxicity.This
allowsyoutofocusoncompoundswithabetterpossibilityofsuccess,loweringdevelopmentcostsandtime.
▸PersonalizingMedicine:AIalgorithmscananalysereal-worldpatientdatatoassistselectthebesttreatment
optionforapatient.
▸AIcanhelpmanageclinicaldataforpharmaceuticalproducts.
▸Patientengagementwithpersonalizedhealthinformation,supportandeducationusingAIchatbotsandvirtual
assistants.
19a

25. History
▸AlanTuringandJohnMcCarthy.Turingisconsideredthe“fatherofAI”dueinparttohisworkintroducingthe
TuringTestin1950.
▸PharmaceuticalcompaniesareusingAItechnologytoreducethedrugdiscoveryprocessfrom5-6yearsto
oneyear.
▸By2025,AIapplicationshavethepotentialtogenerate$350billionto$410billioninyearlyvaluefor
pharmaceuticalbusinesses.
▸AIwillextractimportantinformationfromapatient'selectronicfootprint.
▸AI-enabledheartdevicesincludeelectronicstethoscopesandsoftwarethatuseselectrocardiogramdatatodetect
heartarrhythmiasorsignsofheartfailurearealreadyinuse.
19b

26. Artificial Intelligence and acceleration of drug development
1.Predicting Drug Candidates: AI algorithms (a set of instructions for solving a problem or
accomplishing a task) can predict potential drug candidates faster than traditional methods.
2.Enhancing Precision Medicine: Tailoring treatments to individual genetic profiles.
3.Improving Clinical Trials: Optimizing trial design and patient selection.
4.Data Analysis: Handling vast amounts of research data more efficiently.
5.AI and ML are not just tools but game-changers, making the drug discovery process quicker,
cheaper, and more effective.
20

27. AI for Drug Discovery
21
Who ?
▸1.TargetIdentification:Tofindpossibletherapeutictargets,AIsystemscanexamineavarietyof
datatypes,includinggenomic,proteomic,andclinicaldata.AIaidsinthedevelopmentofdrugs
thatcanalterbiologicalprocessesbyidentifyingtargetsandmolecularpathwayslinkedto
disease.
▸2.VirtualScreening:AImakesitpossibletoeffectivelyscreenenormouschemicallibrariesin
ordertofinddrugcandidatesthatarehighlylikelytobindtoaparticulartarget.AIsavestimeand
moneybyhelpingresearchersprioritiseandchoosecompoundsforexperimentaltestingby
modellingchemicalinteractionsandforecastingbindingaffinities.

28. AI for Drug Discovery
22
Who ?
▸3.Structure-ActivityRelationship(SAR)Modelling:Artificialintelligence(AI)modelsareable
tocreateconnectionsbetweenacompound'schemicalmakeupandbiologicalactivity.This
enablesscientiststocreatecompoundswithdesiredproperties,suchhighpotency,selectivity,
andadvantageouspharmacokineticprofiles,inordertooptimizetherapeuticprospects.
▸4.DeNovoDrugDesign:AIalgorithmscansuggestnewchemicalstructuresthatresemble
drugsbyusinggenerativemodelsandreinforcementlearning.AIbroadensthechemical
universeandhelpscreatenoveldrugideasbylearningfromchemicallibrariesandexperimental
data.

29. AI for Drug Discovery
23
How ?
▸5.OptimizationofDrugCandidates:Bytakingintoaccountanumberofvariables,suchas
pharmacokinetics,safety,andefficacy,AIalgorithmsareabletoevaluateandoptimizedrug
candidates.Thisaidsscientistsinoptimisingmedicinalcompoundstoincreaseefficacywhile
loweringthepossibilityofadverseeffects.
▸6.DrugRepurposing:AImethodscanexaminevastamountsofbiomedicaldatatofindmedications
thatarealreadyonthemarketandmaybeusefulintreatingvariousillnesses.AIspeedsupand
lowersthecostofdrugresearchbyrepurposingcurrentmedicationsfornewapplications.
▸7.ToxicityPrediction:Byexaminingacompound'schemicalmakeupandproperties,artificial
intelligence(AI)algorithmsareabletoforecastadrug'stoxicity.Toxicologicaldatabasescanbeused
totrainmachinelearningalgorithmsthatcandetectdangerousstructuralcharacteristicsorpredict
negativeeffects.Thisaidsscientistsinprioritizingsafercompoundsandreducingthepossibilityof
negativeclinicaltrialreactions.

30. Popular AI model tools used for drug discovery
24
DeepChem:Anopen-sourcelibrarythatoffersavarietyofdrugdiscoverytoolsandmodels,suchas
deeplearningmodelsforgenerativechemistry,virtualscreening,andmolecularpropertyprediction.
RDKit:Apopularopen-sourcecheminformaticstoolkitthatprovidesanumberoffeaturesfor
managingmolecules,searchingsubstructures,andcalculatingdescriptors.Drugdiscoveryappscan
incorporateitwithmachinelearningframeworks.
ChemBERTa:Alanguagemodelcreatedespeciallyfortasksinvolvingdrugdevelopment.Itcanproduce
molecularstructures,forecastcharacteristics,andaidwithleadoptimizationbecauseitispre-trainedon
asizablecorpusofchemicalandbiomedicalliteratureandisbasedontheTransformerarchitecture.

31. ▸GraphConv:Anarchitecturefordeeplearningmodelsthatworkswithmoleculargraphs.By
usingthestructuralinformationcontainedinthegraphrepresentationofmolecules,ithas
provedsuccessfulinforecastingmolecularcharacteristicsliketoxicityandbioactivity.
▸AutoDockVina:Awell-knowndockingprogramthatpredictsthebindingaffinitybetweensmall
compoundsandproteintargetsusingmachinelearningapproaches.Itcanhelpwithlead
optimisationandvirtualscreeningfordrugdiscovery.
▸SMILESTransformer:AdeeplearningmodelthatcreatesmolecularstructuresfromSimplified
MolecularInputLineEntrySystem(SMILES)strings.Leadoptimisationanddenovodrugdesign
aretwoapplicationsforit.
25
Popular AI model tools used for drug discovery

32. ▸SchrödingerSuite:Anall-inclusivedrugdiscoverysoftwaresuitethatincludesanumberofAI-
poweredcapabilities.Predictivemodelling,ligand-basedandstructure-baseddrugdesign,virtual
screening,andmolecularmodellingareamongitsmodules.
▸IBM RXN for Chemistry : An AI model intended to forecast chemical reactions. It helps with drug
synthesis and the development of new synthetic pathways by generating possible reaction
outcomes using deep learning algorithms and sizable reaction databases.
▸Scape-db(Extraction of Chemical and Physical Properties from the Literature-DrugBank):
A database which uses machine learning and natural language processing to extract biological
and chemical information from scholarly publications. It offers useful data for studies on
medication discovery.
26
Popular AI model tools used for drug discovery

33. 27
GENTRL(GenerativeTensorialReinforcementLearning):Adeeplearningmodelthatcreatesnew
moleculeswithdesiredcharacteristicsbyfusinggenerativechemistryandreinforcementlearning.De
novodrugdesignandoptimizationhavemadeuseofit.
Popular AI model tools used for drug discovery

34. 28
Five significant applications of AI in drug development are highlighted in the Deloitte Intelligent
Drug development report:
1. Target identification (28 percent of all solutions);
2. Screening small molecular libraries to find new candidates (40 percent);
3. De novo drug design (8 percent),
4. Drug repurposing (17 percent), and
5. Preclinical studies (7 percent).
AI use cases in drug discovery

35. 29
ExamplesofAIdrugs:Anastrozole,Letrozole,andExemestane
Interesting facts
Phase1trialsforAI-discovereddrugshaveshownsuccessratesbetween80-90%,significantlyhigher
thanthehistoricalindustryaveragesof40-65%,”
AIMostCommonApplicationsincludediagnosingpatients,end-to-enddrugdiscoveryand
development,improvingcommunicationbetweenphysicianandpatient,transcribingmedical
documents,suchasprescriptions,andremotelytreatingpatients.
Denovodrugdesign:amedicineagainstfibrosiswasgeneratedinjust21days

36. Interesting facts contd…
30
How ?
▸Exscientiaisaprecisionmedicinecompanypoweredbyartificialintelligencethatiscommittedtofinding,
developing,andproducingthebestmedicationsinthemostefficientmanner.
▸InordertoprovideacomprehensivesolutionforAI-generatedmethods,virtualscreeningofvastchemical
spaces,andhit-to-leaddiscoveryandoptimization,AIDDISON
TM
drugdiscoverysoftwareintegratesAI,
machinelearning,andCADDmethodology.
▸Researcherscanexploreanunlimitedchemicaluniverseandgenerateconceptsforentirelynewmolecules
withthisAI-powereddrugdevelopmentsoftware.Basedonanticipatedactivity,AIDDISONTMcanswiftly
identifycompoundsthatshowpromiseasdrugs.Theprogrampredictswhetherasubstancemaybe
producedbychemicalsynthesisbyusingasyntheticaccessibilityscorederivedfromourSYNTHIA
TM
retrosynthesissoftware.

37. CONCLUSION
37
Artificialintelligence(AI)israpidlychangingthedrugdevelopmentprocessbyincreasingthespeed,accuracy,andefficiency
ofthediscoveryprocess.
AItoolsareusedatmanylevels,suchasinthedevelopmentofnewdrugsandtheenhancementofclinicaltrialdesigns.
Inadditiontoacceleratingthediscoveryprocess,artificialintelligencehasthepotentialtochangetheeconomicsof
medicationdevelopment.
Largepopulationdatacanbeanalysedusingartificialintelligence(AI)algorithmstoidentifytrendsandpatternsthatmay
assistforecasthowwellprospectivemedicationcandidateswillworkforparticularpatientgroups.Thismakesitpossibleto
tailormedicationstoeachperson'suniqueneeds.
What'sUpNextforPharmaceuticalR&D?
Inconclusion,pharmaceuticalresearch&developmentisasignificantandever-evolvingsectorthatisalwaysadaptingto
meetthemodernhealthissues.FromintegratingnewtechnologylikeAIandBigDatatoadheringtolegalandethical
standards,pharmaceuticalresearchanddevelopmentisattheforefrontofmedicalinnovation.Drugresearchisexpectedto
becomeincreasinglyintegrated,patient-centered,andtechnology-driveninthefuture,accordingtotrends,funding
patterns,anditsglobaleffect.Asweturntothefuture,thepromiseofR&Dtoimproveglobalhealthremainsabeaconof
progressandoptimism.
Between2015and2019,theFDAapprovedalmosttwiceasmanynewdrugsasithadinthepreviousdecade.Theproportion
ofauthorisedbiologicsbytheFDA.
31

38. References
1.DiMasiJ.A.,FeldmanL.,SecklerA.,WilsonA.Trendsinrisksassociatedwithnewdrugdevelopment:
successratesforinvestigationaldrugs.ClinPharmacolTher.2010;87:272–277.doi:
10.1038/clpt.2009.295.
2.DiMasiJ.A.,GrabowskiH.G.,HansenR.W.Innovationinthepharmaceuticalindustry:newestimatesof
R&Dcosts.JHealthEcon.2016;47:20–33.doi:10.1016/j.jhealeco.2016.01.012.
3.PaulS.M.,MytelkaD.S.,DunwiddieC.T.,PersingerC.C.,MunosB.H.,LindborgS.R.HowtoimproveR&D
productivity:thepharmaceuticalindustry'sgrandchallenge.NatRevDrugDiscov.2010;9:203–214.doi:
10.1038/nrd3078.
4.CummingsJ.,MorstofT.,ZhongK.Alzheimer'sdiseasedrugdevelopmentpipeline:fewcandidates,
frequentfailures.AlzheimersResTher.2014;6:37–44.doi:10.1186/alzrt269.
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39. 32
THANK You!
Any questions?
You can find me at
[email protected] &
contact me at [phone]
Dr. Rajasekhar Reddy Poonuru
Professor & Head, Department of Pharmaceutics
St. Peter’s Institute of Pharmaceutical
Sciences, Hanumakonda
“Any unknown in the practice field is a potential research idea”