人工智能对仿制药行业业务发展的转型

Contents01/Executive Summary02/Introduction03/Challenges in Generics Business Development (Pre-AI)904/AI as a Game-Changer for Generics BD05/A case example06/Technical Foundations: How AI Models Drive BD Insights07/Real-World Impact and Looking Ahead08/Conclusion 251323273236 Executive Summary01/ Executive Summary01/Business development (BD) teams in the genericspharmaceutical industry face high-stakes decisionsas blockbuster drugs lose exclusivity (LOE) andnew opportunities emerge. AI is revolutionising thisprocess by providing data-driven insights that werepreviously unattainable with manual methods.This white paper explores how AI helps BD teams:Evaluatecandidatemoleculesforgenericdevelopmentbyscanningvastdatasources(patents,clinicaltrials,salesdata)toidentifyhigh-potentialopportunitiespost-LOE.Understandtotaladdressablemarketsizeforeachmoleculebyaggregatingandanalysingglobaldemandsignals,tenderadoptionratesandtendernetprices,prescriptionvolumes,andsalesfigures.AnalysenetpriceevolutionafterLOEusingpredictivemodelsthatlearnfromhistoricalLOEcaseswithmoregranularML/Deeplearning-basedanalogselectionmethodstoforecastpriceerosionandcompetitivedynamics.BuildrobustbusinesscaseswithAI-drivenforecastingofrevenuesandmarketshare,enablingconfidentinvestmentdecisionsandstrategyformulation. We highlight Vamstar’s AI capabilities as a case example of this transformation– focusing on technical insights and real-world applications. Real case studiesillustrate how top generics companies have leveraged AI for competitive advantage,such as boosting tender win rates and accurately forecasting post-LOE marketdynamics. We also delve into the AI models and machine learning techniquesunderpinning these solutions, from natural language processing (NLP) to time-series forecasting and knowledge graphs.Value for StakeholdersThis paper offers BD teams a framework for integrating AI into their decision-making, provides investors with indicators of how AI improves ROI in genericsdevelopment, and informs industry stakeholders of the evolving best practices atthe intersection of AI and generics business strategy. The goal is a knowledge-drivenoverview of AI’s transformative impact on generics BD activities, balancing technicaldepth with practical relevance.01 / Executive Summary / continued 02/Introduction:BD Function in aChanging Landscape 02/Introduction:BD Function in aChanging LandscapeThe generics pharmaceutical sector is intensely competitive and data-intensive.As patents on brand-name drugs expire, generics companies rush to evaluatewhich molecules are worth developing. The stakes are high – global pharmaceuticalfirms face billions in lost sales due to patent expirations in coming years. For BDteams, each impendingloss of exclusivity (LOE)is both a threat to incumbentsand an opportunity for generics manufacturers. Identifying and capitalising on theright opportunities requires a deep understanding of markets and foresight intohow those markets will evolve post-LOE.ChallengesTraditionally, BD teams relied on manual data gathering and past experienceto make decisions. They would sift through patent registries to find LOE dates,purchase costly market reports for sales data, and make educated guesses aboutfuture pricing. This process is slow and prone to error, given the sheer volume ofdata and the dynamic nature of pharmaceutical markets. Key questions for a BDteam include: 02 / Introduction: BD Function in a Changing Landscape / continuedWhichmolecules(drugs)losingpatentprotectioninthenextfewyearsshouldwetargetforgenericdevelopment?Whatisthetotalmarketsizeforeachofthesemolecules(currentbrandsales,volumeusage,etc.)?Howwillpricesandmarketshareshiftoncegenericsenter–i.e.,howmuchpriceerosionwilloccurandhowmanycompetitorswilltherebe?Canweprofitablyproduceandsellagenericversiongiventheexpectedpricedropsandcompetition?Answering these questions accurately is essential for building a solidbusiness casefor each potential generic product. Even a slight misjudgmentin market size or price erosion can mean the difference between a lucrativeproduct launch and a costly failure.AI Enters the ArenaAdvances in artificial intelligence offer BD teams powerful tools to handle thesechallenges. By leveraging AI algorithms on large datasets, teams can move fromgut-feel and static spreadsheets to evidence-based, real-time decision support.AI can processbillionsofdata points(e.g., drug sales, clinical data, payer records,tenders) and detect patterns or signals that humans might miss. In an industrywhere timing and information are critical, AI’s ability to rapidly analyse data andgenerate predictions is transformative. This paper will detail how AI aids BD teams at each step – from molecule selectionto market analysis and pricing strategy – and will illustrate these benefits throughVamstar’s capabilities and real-world case studies. We maintain a focus on technicaland practical insights, showinghowthe AI works andwhat valueit delivers, w