人工智能与机器学习在制药行业：重塑预测格局

CLICK TO EDIT MASTERTITLE STYLE AI and ML in Pharma: Redefining theForecasting Landscape Today’s Presenters DAVID JAMESFounder,J+D Forecasting DANIEL CHANCELLOR STEFANO DRIUSSI VP Thought Leadership,Evaluate Head of Software Engineering, J+D Forecasting 20+ years of experiencesupporting Pharmaceutical andBiotech companieswith theirforecasting needs. Evaluate,aNorstellaCompany By combiningEvaluate'sworld-classconsensus forecasting and consultingexpertise with J+D Forecasting'sspecialisedmodels, delivered through cloud-basedmanagement and analytical solutions,clientscan achieve a comprehensive ▪Expertsin all pharmaceutical forecastingmethodologies. Usinginnovative ▪Led by asenior levelteam of forecasting,market research, technical and data analysis ▪Advanced technical capabilities, havingdeveloped over1,000 forecast modelsanddeployed our FC+ software and FC365 General Overview Application of AI in Pharma Practical uses of AI in Forecasting AI Revolution–What to Expect Q&A AI in Pharma Forecasting–Challenges and Opportunities General Overview “A.I. could be ‘more profound’than both fire and electricity” Sundar Pichai,CEO Alphabet Different areas ofArtificial Intelligence. AI in Pharma Forecasting–Challenges and Opportunities Application of AI in Pharma Application ofAI in Pharma •Globally valued at ~$905 million in 2021–($9,241 million by 2030) •50% of global healthcare companies plan to implement AI strategies (by 2025) •AI-driven new drug development expected to grow 40% annually–($4bn in 2024)AI-driven AI Applications in Pharma AI and ML in Pharma:Redefining the ForecastingLandscape AI withinDrug Discovery Artificial intelligence has many implications for research, drugdiscovery and development and trials: •Identifynew drug moleculesthat have so far eluded scientists Already making drug discovery fasterand cheaper, with a number ofmultiple AI-designed drugs now being •Synthetic data can bridge population data gaps •Increase the probability ofsuccess of NCE’s •Lower R&D costs& reduced time to market AI within Drug Discovery:Forecasting Implications Impact: Est. around 270 companies currentlyworking on AI-driven drug discovery. ▪The future competitive environment will change asmore drugs are discovered.▪An increase in partnership deals between industryand vendors. Lack of in-house AI expertise drivingvendor partnerships: ▪GSK has partnered with CloudPharmaceuticals and InsilicoMedicine to utilize their AI platformsfor target identification, drug ▪Sanofi partnered withAtomwisetodiscover and synthesise drugcompounds for five different targets,paying $20 million upfront for theirinnovation and AI capabilities. AI and ML in Pharma: Redefining the ForecastingLandscape AI within Clinical Trials. Benefits of AI application: AI can help to make clinical trials moreefficient,more accurate, and more effective. Patient recruitment and screening AI can help to reduce the time and cost of clinical trials,and it can also help to ensure that the right patients are Data collection and analysis ▪Around 90% of clinical trials run significantlyover time or over budget.▪86% of clinical trials fail to recruit enoughpatients within their target time frame.▪Between 25% to 40% of trials will fail because AI can help to identify patterns and trends in the data,plus identify patients who are most likely to benefit from a Risk assessment AI can help to ensure the safety of patients, and it canalso help to identify patients who are at risk of Predictive modelling AI can help develop predictive models to identify trialsthat are most likely to be successful, and those that aremost likely to fail. Decision support Success Time to Key financemetrics AI can help investigators to make informed decisionsabout the design, conduct, and interpretation of clinical AI within Clinical Trials:Forecasting Implications. Impact: Potential to revolutionise the way clinical trialsare conducted-estimated 50% reductionin ▪Decreasing drug development time and cost will requireadjustments to forecast assumptions around probability of ▪Improved, faster recruitment for clinical trials,reducing overall trial length and potential costs. ▪Creating a more effective use of R&D budget. AI and ML in Pharma: Redefining the ForecastingLandscape AI withinDiagnostics AI in practice: Has the potential to make healthcare moreaccessible and affordable–plus enhancing Challenges AI can address: ▪Identifyingat risk populationsfor earlyintervention.▪Diagnosisand decisions about treatmentplans.▪Personalised treatment based on patients’ AI Detecting Heart Disease AI test providing higher diagnosticaccuracy, reduces the need forunnecessary invasive angiograms by Credit: NVIDIA AI within Diagnostics:ForecastingImplications Impact: AI techniques are already being used to diagnosenumerous diseases. ▪Earlier diagnosis of diseases has implications onpatient outcomes, therefore change patientdistribution a