Biopharmaceutical Informatics: Learning to Discover Developable Biotherapeutics
暫譯: 生物製藥資訊學：學習發現可開發的生物治療藥物
Kumar, Sandeep, Nixon, Andrew
相關主題
商品描述
Despite the phenomenal clinical success of antibody-based biopharmaceuticals in recent years, discovery and development of these novel biomedicines remains a costly, time-consuming, and risky endeavor with low probability of success. To bring better biomedicines to patients faster, we have come up with a strategic vision of Biopharmaceutical Informatics which calls for syncretic use of computation and experiment at all stages of biologic drug discovery and pre-clinical development cycles to improve probability of successful clinical outcomes. Biopharmaceutical Informatics also encourages industry and academic scientists supporting various aspects of biotherapeutic drug discovery and development cycles to learn from our collective experiences of successes and, more importantly, failures. The insights gained from such learnings shall help us improve the rate of successful translation of drug discoveries into drug products available to clinicians and patients, reduce costs, and increase the speed of biologic drug discovery and development. Hopefully, the efficiencies gained from implementing such insights shall make novel biomedicines more affordable for patients.
This unique volume describes ways to invent and commercialize biomedicines more efficiently:
- Calls for digital transformation of biopharmaceutical industry by appropriately collecting, curating, and making available discovery and pre-clinical development project data using FAIR principles.
- Describes applications of artificial intelligence and machine learning (AIML) in discovery of antibodies in silico (DAbI) starting with antigen design, constructing inherently developable antibody libraries, finding hits, identifying lead candidates, and optimizing them.
- Details applications of AIML, physics-based computational design methods, and other bioinformatics tools in fields such as developability assessments, formulation and excipient design, analytical and bioprocess development, and pharmacology.
- Presents pharmacokinetics/pharmacodynamics (PK/PD) and Quantitative Systems Pharmacology (QSP) models for biopharmaceuticals.
- Describes uses of AIML in bispecific and multi-specific formats.
Dr Sandeep Kumar has also edited a collection of articles dedicated to this topic which can be found in the Taylor and Francis journal mAbs.
商品描述(中文翻譯)
儘管抗體基礎生物製藥在近年來取得了驚人的臨床成功，但這些新型生物藥物的發現和開發仍然是一項成本高昂、耗時且風險極高的工作，成功的機率相對較低。為了更快地將更好的生物藥物帶給患者，我們提出了一個生物製藥資訊學的戰略願景，呼籲在生物藥物發現和臨床前開發的各個階段，綜合運用計算和實驗，以提高成功臨床結果的機率。生物製藥資訊學還鼓勵業界和學術科學家支持生物治療藥物發現和開發周期的各個方面，從我們共同的成功經驗以及更重要的失敗中學習。從這些學習中獲得的見解將幫助我們提高藥物發現轉化為臨床醫生和患者可用藥物的成功率，降低成本，並加快生物藥物的發現和開發。希望通過實施這些見解所獲得的效率，能使新型生物藥物對患者來說更加可負擔。
這本獨特的著作描述了更有效地發明和商業化生物藥物的方法：
- 呼籲生物製藥行業進行數位轉型，通過適當收集、整理和提供發現及臨床前開發項目數據，遵循FAIR原則。
- 描述人工智慧和機器學習（AIML）在抗體發現中的應用，從抗原設計開始，構建內在可開發的抗體庫，尋找命中，識別主導候選者，並對其進行優化。
- 詳細介紹AIML、基於物理的計算設計方法及其他生物資訊工具在可開發性評估、配方和輔料設計、分析和生物過程開發以及藥理學等領域的應用。
- 提出生物製藥的藥物動力學/藥效學（PK/PD）和定量系統藥理學（QSP）模型。
- 描述AIML在雙特異性和多特異性格式中的應用。
Sandeep Kumar博士還編輯了一系列專門針對這一主題的文章，這些文章可以在Taylor and Francis期刊《mAbs》中找到。
作者簡介
Dr. Sandeep Kumar is currently a Distinguished Fellow (Executive Director) at the department of Computational Science in Moderna Therapeutics, Cambridge, MA where he leads Molecular Design and Modeling team. Sandeep Kumar holds a Ph.D. in Computational Biophysics and has over 25 years of experience researching protein structure - Function relationships. Sandeep Kumar has so far contributed towards more than 100 research articles, reviews, book chapters, and has previously edited a book entitled "Developability of Biotherapeutics: Computational Approaches". Sandeep has been contributing towards discovery and development of numerous monoclonal antibodies, antibody drug conjugates, bispecific and multi-specific modalities, as well as vaccines. Based on the insights gained from these experiences, Sandeep has been advocating for Biopharmaceutical Informatics, a strategic vision dedicated to synergistic use of computation and experimentation towards a cost effective and more efficient discovery and development of Biotherapeutics. More recently, he is promoting the concept of DAbI (Discovery of Antibodies in silico) where he sees an opportunity for generative AI to not only accelerate biopharmaceutical drug design but also to expand the antigen space druggable by antibody-based biotherapeutics.
Dr. Andrew Nixon is currently Vice President, Biotherapeutics Molecule Discovery at Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA. Andy earned his Ph.D. in Physical Biochemistry from the University of London for studies completed at the MRC's National Institute for Medical Research. Andy has over 20 years of experience in biologic drug discovery and has contributed to over 100 antibody discovery programs resulting in numerous clinical candidates and approved biologics including TAKHZYRO, a fully human antibody inhibitor of plasma kallikrein.
作者簡介(中文翻譯)
桑迪普·庫馬博士目前是位於麻薩諸塞州劍橋的Moderna Therapeutics計算科學部的傑出研究員（執行董事），他領導分子設計與建模團隊。桑迪普·庫馬擁有計算生物物理學的博士學位，並在蛋白質結構與功能關係的研究方面擁有超過25年的經驗。至今，桑迪普·庫馬已經發表了超過100篇研究文章、評論、書籍章節，並曾編輯一本名為《生物治療藥物的可開發性：計算方法》的書籍。桑迪普一直在貢獻於多種單克隆抗體、抗體藥物偶聯物、雙特異性和多特異性模式以及疫苗的發現與開發。基於這些經驗所獲得的見解，桑迪普一直在倡導生物製藥資訊學，這是一個專注於計算與實驗協同使用的戰略願景，旨在實現生物治療藥物的成本效益和更高效的發現與開發。最近，他正在推廣DAbI（抗體的計算發現）概念，他認為生成式人工智慧不僅能加速生物製藥藥物設計，還能擴展抗體基生物治療藥物可作用的抗原空間。
安德魯·尼克森博士目前是Boehringer Ingelheim Pharmaceuticals, Inc.（美國康涅狄格州里奇菲爾德）生物治療藥物分子發現的副總裁。安迪在倫敦大學獲得物理生物化學博士學位，研究是在MRC國家醫學研究所完成的。安迪在生物藥物發現方面擁有超過20年的經驗，並參與了超過100個抗體發現計劃，這些計劃產生了多個臨床候選藥物和獲批的生物藥物，包括TAKHZYRO，一種完全人源的血漿激肽酶抑制劑。
