Significance of Analytics in Biosimilars
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Biosimilars are biological products that are composed or produced in living systems such as yeast or bacteria. They are defined as the medicinal drugs which function exactly like certain existing and approved drugs, often referred to as “reference product”. The major facets which make up a biosimilar are that they are “highly similar” to their reference product and they should have no clinically significant differences from safety, efficiency, and functionality perspectives...
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Since 2009 the Guide to PHARMACOLOGY database (GtoPdb) team have curated 7586 ligands from papers, including approved drugs, clinical candidates , research compounds peptides and clinical antibodies (PMID 24234439). As PubChem pushes towards 70 million compound identifiers (CIDs), we have noticed the problem
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Abstract
Databases of bioactive compounds are crucial for pharmacology, drug discovery and chemical genomics as public sources approach ~ 100 million records. However, in recent years this famine-to-feast presents difficulties for searching chemical structures and linked activity data, particularly for those unfamiliar with the constitutive challenges of molecular representation in silico (PMID 25415348). A key problem is entries of structural variants of “the same thing” as pharmacological entities (i.e. representational multiplexing). For example, a 2009 comparison of three database subsets of ~1200 approved drugs recorded only 807 structures in-common (PMID 20298516). In addition, published counts of approved drugs vary widely. These issues have been continually encountered by the Guide to PHARMACOLOGY database (GtoPdb) team that, since 2009, has achieved the curation of ~5500 small molecules (including approved drugs) from papers. Concomitantly, we have noticed an increase in multiplexing as PubChem pushes towards 65 million compound identifiers (CIDs). Since one of our key objectives is to affinity-map ligands to their targets, we decided to assess this multiplexing problem in order to optimise our curation rules. The results have implications for the entire bioactivity information space. We began by compiling CID sets for seven different sources within PubChem encompassing approved drugs. Initially a 7x7 pairwise comparison matrix indicated low overlap between these sources. A Venn diagram was then made from the approved drug CIDs mapped by DrugBank, Therapeutic Target Database and ChEMBL. At 749, the three-way intersect was less than 35% of the union of all CIDs covered by the sets. Strikingly, this looks worse that the 2009 study (although the sources and comparison methods were different). We will present further analyses that go some way towards explaining these results. One of these is determining “same connectivity” statistics inside PubChem as a measure of multiplexing. For DrugBank, each approved drug was related to, on average, 19 different CIDs as structural variants. Analysis of multiplexing confirmed trends we had observed during individual drug curation. This included ~ 30% stereoisomer enumerations but, surprisingly, ~70% isotopic derivatives, dominated by patent-derived virtual deuteration. We also established the ratio of submissions (SIDs) to CIDs was 78. The paradox was that, despite this high “majority vote” support for approved drug CIDs curated by DrugBank, only 55% were in the 3-way consensus (figures for the other two curated sources were similar). Analysing by year in PubChem indicated how the recent expansion of vendor and patent-extraction structures contributes to both multiplexing and the SID: CID ratio. While approved drugs are strongly impacted, associated problems, such as split activity data and deciding the “correct” structures, affect essentially all public drug discovery chem
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Abstract
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Synthesize or buy?
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"Data sources on DiscoveryGate are indexed. This allows synthetic chemists to quickly determine whether a chosen synthesis is cost-justified or prohibitive. Access to primary literature and associated Material Safety Data Sheets (MSDS) provides information on determining factors such as yield and safety."
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A resource for the selection and interpretation of cell-based perturbogens: the IUPHAR/BPS Guide to PHARMACOLOGY
Christopher Southan, Elena Faccenda, Joanna L. Sharman, Adam J. Pawson, Simon D. Harding, Jamie A Davies,
Translational research requires the integration of the in vitro molecular mechanisms of action (mmoa) of small molecules, cell-based screening studies, animal models and eventual clinical trials. The International Union of Pharmacology (IUPHAR)/British Pharmacology Society (BPS) database, GtoPdb http://www.guidetopharmacology.org/ provides expert-annotated molecular interactions between endogenous receptor ligands, probes, lead compounds, clinical drugs and their protein targets. It thus provides a core set of quantitative pharmacological relationships that can be interrogated for many purposes, including those running cell-based screens, not only during result interpretation but also to identify key compounds for scoping and consolidation experiments. As described in [1] GtoPdb is populated by records extracted from pharmacology and medicinal chemistry journals, and released quarterly. Quality is ensured by curatorial stringency and our unique model of content selection based on recommendations from IUPHAR target class subcommittees of international experts collaborating with the in-house curators. The database now has over 14 000 binding values (mainly IC50, Ki or Kd) between 8000 ligands and 15000 human proteins (mainly primary but also secondary off-target interactions) representing a 7% druggable proteome. Our coverage is complementary to other sources. For example the 6565 structures we recently submitted to PubChem as CIDs, 5206 were not in DrugBank and 1535 not in ChEMBL. This includes recommended tool compounds with relatively defined mmoa (including 110 from the Structural Genomics Consortium Probe Portal). We also have 75% overlap with vendors for procurement and 80% with patent extractions that in many cases allow mapping to SAR data sets from first-filings (some of which we point to). In a cell screening context 1254 of our targets intersect with proteins in the Reactome pathway database. This is one way to select chemical peturbation points that could be detected by assay readouts. From Nov 2015 we have been funded by the Wellcome Trust to extend into immunopharmacology (within the existing database schema) that is now driving overall GtoPdb content expansion. Parties engaged in cell based assays using or could use compounds we have are encouraged to use GtoPdb, contact us for queries, possible analogue expansions and/or alert us to prospective new content. [1] Southan C et. al. (2016) Nucleic Acids Res. 44(D1):D1054-68, PMID: 26464438
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There is a growing need for rapid chemical screening and prioritization to inform regulatory decision-making on thousands of chemicals in the environment. We have previously used high-resolution mass spectrometry to examine household vacuum dust samples using liquid chromatography time-of-flight mass spectrometry (LC-TOF/MS). Using a combination of exact mass, isotope distribution, and isotope spacing, molecular features were matched with a list of chemical formulas from the EPA’s Distributed Structure-Searchable Toxicity (DSSTox) database. This has further developed our understanding of how openly available chemical databases, together with the appropriate searches, could be used for the purpose of compound identification. We report here on the utility of the EPA’s iCSS Chemistry Dashboard for the purpose of compound identification using searches against a database of over 720,000 chemicals. We also examine the benefits of QSAR prediction for the purpose of retention time prediction to allow for alignment of both chromatographic and mass spectral properties. This abstract does not reflect U.S. EPA policy.Antibody drug conjugate market opportunity analysis
The Antibody Drug Conjugates market will definitely witness an increase in the partnership and collaborative agreements to develop these drugs. It has been observed that the process of drug development is highly correlated to basic research in Biology and Medicine. Specifically, the details about target molecules and their functions along with their involvement in pathology generally stem from basic research which is conducted in universities and public research institutes. The recent years have thus witnessed a growing relationship between the pharma companies and the academic institutions. Majority of the pharmaceutical companies across the globe have been reducing the size of their departments for basic research. Instead they have started to depend heavily on venture companies and universities, which act as the major source of knowledge.
“Antibody Drug Conjugate Market Opportunity Analysis” Report Highlights:
Global ADC Market Insight
ADC Patent Analysis
Orphan Status & ADC
Favorable Market Drivers & Key Issues to be Discussed
ADC Clinical Trial Insight by Phase & Target Indications
ADC Profiles in Report: 201
Majority of ADC in Preclinical Phase: 83
Marketed ADC Clinical Profiles
Multiplexing analysis of 1000 approved drugs in PubChem
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Bioinformatics
The drug discovery process involves several steps:
1) Identifying a therapeutic target through understanding disease mechanisms.
2) Discovering lead compounds that show activity against the target through high-throughput screening.
3) Optimizing lead compounds to improve properties like efficacy, safety, and pharmacokinetics using techniques like molecular modeling and combinatorial chemistry.
4) Conducting preclinical and clinical testing in multiple phases to determine safety and efficacy in humans.
The IUPHAR/MMV Guide to Malaria Pharmacology
This document summarizes the creation of the IUPHAR/MMV Guide to Malaria Pharmacology (GtoMPdb) database by the authors. It captures antimalarial compounds, targets, and their relationships by curating data from publications. The database has adapted the Guide to Pharmacology data model and has begun capturing data on 28 antimalarial ligands. Future plans include expanding the curation, developing an online portal, and submitting data to PubChem to link compounds to publications and make the data more accessible.
Lecture 1 –Introduction to drug design and development
This document provides an overview of a lecture on drug design and development. It discusses four units that will be covered: drug design and development, drug targeting and identification, pharmacophore modeling, and computer aided drug design. It provides definitions of key terms like drug and pharmacology. It also summarizes the stages of new drug synthesis: drug discovery, pre-clinical development, and clinical development. Properties of an ideal drug and the role of bioinformatics in drug discovery are also briefly outlined.
Chemo informatics scope and applications
This document discusses the scope and applications of chemoinformatics. It outlines how chemoinformatics is used in drug design, clinical research, synthetic chemistry, pharmaceutical industries, pharmacogenomics, systems biology, and nanotechnology. Specifically, it describes how chemoinformatics provides virtual structure libraries, docking capabilities, and QSAR studies to aid in drug discovery and development. It also notes applications such as storage and retrieval of chemical data, common file formats, creation of virtual libraries, virtual screening, and using QSAR to predict compound activities from their structures.
Antibody Drug Conjugates - Survey
Sponsored by Waters, Bioanalysis Zone explored the analysis of antibody drug conjugates (ADCs), the unique bioanalytical challenges they pose, and how those challenges are being addressed.
Role of computer in the discovery of drugs
Computer-aided drug design (CADD) uses computational techniques like molecular modeling and simulation to aid in drug discovery and development. The goal of CADD is to identify and optimize lead compounds to reduce experiments. CADD techniques depend on bioinformatics tools and databases to store and analyze structural and activity data on drug molecules and biological targets. This allows CADD to speed up key steps like identifying disease targets and potential drug candidates in silico before experimental validation.
Drug discovery Using Bioinformatic
Structural bioinformatics uses computational techniques to aid in drug discovery. Bioinformatics analyzes gene and protein sequence data to identify potential drug targets. Molecular docking then simulates how candidate drug compounds interact with these targets at the atomic level. This provides insight into how well a compound may bind to and affect the target. The results can help optimize drug candidates prior to further testing.
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Conducting a Clinical Trial is a complex process, consisting of activities such as protocol preparation, site selection, approval of various authorities, meticulous collection and management of data, analysis and reporting of the data collected
Each activity is benefited from the development of point applications which ease the process of data collection, reporting and decision making. The recent advancements in mobile technologies and connectivity has enabled the generation and exchange of a lot more data than previously anticipated. However, the lack of interoperability and proper planning to leverage this data, still acts as a roadblock in allowing organizations truly harness their data assets. This document will help life sciences IT professionals and decision makers understand challenges and opportunities around clinical data integration
Biosimilars: Regulatory and Clinical Considerations
Other considerations for clinical studies are that the regulations are written with some flexibility, and although clinical studies have been required thus far, they are not mandated by the regulations. Perception from sponsors is that innovators make process changes all the time that impact structure and no clinical study is done because of good analytical characterization so why are biosimilars different? With solid analytical and functional data, we should continue to challenge regulators on the need for clinical studies.
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The document discusses the applications of bioinformatics in drug discovery. It describes how bioinformatics supports computer-aided drug design through computational methods to simulate drug-receptor interactions. It also discusses how virtual high-throughput screening can identify compounds that strongly bind to protein targets. The document outlines the key steps in drug design, including identifying the disease target, studying lead compounds, rational drug design techniques, and testing drugs. It emphasizes that bioinformatics can predict important drug characteristics like absorption and toxicity to save costs during development.
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Application of Machine Learning in Drug Discovery and Development Lifecycle
Machine learning and Artificial Intelligence have significantly advanced in recent years owing to their potential to considerably increase the quality of life while reducing human workload. The paper demonstrates how AI and ML are used in the drug development process to shorten and enhance the overall timeline. It contains pertinent information on a variety of Machine Learning approaches and algorithms that are used across the whole drug development process to speed up research, save expenses, and reduce risks related to clinical trials. A range of QSAR analysis, hit finding, and de novo drug design applications are used in the pharmaceutical industry to enhance decision-making. As technologies like high-throughput screening and computation analysis of databases used for lead and target identification and development create and integrate vast volumes of data, machine learning and deep learning have grown in importance. It has also been emphasized how these cognitive models and tools may be used in lead creation, optimization, and thorough virtual screening. In this paper, problem statements and the corresponding state-of-the-art models have been considered for target validation, prognostic biomarkers, and digital pathology. Machine Learning models play a vital role in the various operations related to clinical trials embracing protocol optimization, participant management, data analysis and storage, clinical trial data verification, and surveillance. Post-development drug monitoring and unique industrially prevalent ML applications of pharmacovigilance have also been discussed. As a result, the goal of this study is to investigate the machine learning and deep learning algorithms utilised across the drug development lifecycle as well as the supporting techniques that have the potential to be useful.
Application of Machine Learning in Drug Discovery and Development Lifecycle
Machine learning and Artificial Intelligence have significantly advanced in recent years owing to their potential to considerably increase the quality of life while reducing human workload. The paper demonstrates how AI and ML are used in the drug development process to shorten and enhance the overall timeline. It contains pertinent information on a variety of Machine Learning approaches and algorithms that are used across the whole drug development process to speed up research, save expenses, and reduce risks related to clinical trials. A range of QSAR analysis, hit finding, and de novo drug design applications are used in the pharmaceutical industry to enhance decision-making. As technologies like high-throughput screening and computation analysis of databases used for lead and target identification and development create and integrate vast volumes of data, machine learning and deep learning have grown in importance. It has also been emphasized how these cognitive models and tools may be used in lead creation, optimization, and thorough virtual screening. In this paper, problem statements and the corresponding state-of-the-art models have been considered for target validation, prognostic biomarkers, and digital pathology. Machine Learning models play a vital role in the various operations related to clinical trials embracing protocol optimization, participant management, data analysis and storage, clinical trial data verification, and surveillance. Post-development drug monitoring and unique industrially prevalent ML applications of pharmacovigilance have also been discussed. As a result, the goal of this study is to investigate the machine learning and deep learning algorithms utilised across the drug development lifecycle as well as the supporting techniques that have the potential to be useful.
Applications Of Bioinformatics In Drug Discovery And Process
1. Bioinformatics uses computer science and information technology to analyze biological data and assist with drug discovery. It helps identify drug targets and design drug candidates.
2. The drug design process involves identifying a disease target, studying compounds of interest, detecting molecular disease bases, rational drug design, refinement, and testing. Bioinformatics tools assist with each step.
3. CADD uses computational methods to simulate drug-receptor interactions and is heavily dependent on bioinformatics tools and databases. It supports techniques like virtual screening, sequence analysis, homology modeling, and physicochemical modeling to aid drug development.
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This presentation describes several deficiencies of drug product labels and how information on the semantic web can be used to update the drug product label.
Preparing for a New Time in Medical Literature Monitoring
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Applicationsofbioinformaticsindrugdiscoveryandprocess
Bioinformatics plays an important role in drug discovery and development by enabling target identification, rational drug design, compound refinement, and other processes. Key applications of bioinformatics include virtual screening of large compound libraries to identify potential drug leads, homology modeling of protein structures to inform drug design, and similarity searches to find analogs of existing drug molecules. The overall drug development process involves studying the disease, identifying drug targets, designing compounds, testing and refining candidates, and conducting clinical trials. Computational techniques expedite many steps but experimental validation is still needed.
Biosimilars
Biosimilars are biopharmaceutical drugs that are similar to an existing approved biologic drug (the reference product). Biosimilars undergo a step-wise comparability exercise to demonstrate similarity in structure, function, safety and efficacy to the reference product. Regulatory agencies such as the FDA and EMA require extensive characterization, non-clinical and clinical studies to establish biosimilarity. Guidelines for approval of biosimilars have been established in regions such as Europe, US, Korea, Singapore and India to enable a pathway for approval of biosimilar versions of biologic drugs.
PMS and Outsourcing - Copy.pptx
Post-marketing surveillance involves monitoring drug safety after market release. It identifies rare or long-term adverse effects not seen in clinical trials. Manufacturers must report serious adverse events to regulatory agencies. Outsourcing bioavailability and bioequivalence studies to contract research organizations allows companies to access expertise and resources while reducing costs and time to market. CROs conduct clinical trials, data analysis, and reporting according to Good Clinical Practice standards. Qualification of appropriate CROs involves assessing capabilities, infrastructure, compliance history, and collaboration effectiveness.
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This document discusses the FDA's perspective on regulating cellular therapies. It provides the following key points:
1. The FDA regulates cellular therapies as drugs, biological products, and human cells/tissues under various regulations including cGMP, biological product regulations, labeling regulations, and HCT/P regulations.
2. Cellular therapies present challenges for development and regulation due to their complex nature involving living cells.
3. The document provides tips for navigating FDA regulations during development, including being data-driven, providing complete documentation, being informed of FDA resources, communicating with the FDA, and planning ahead.
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Provide statistical and computational tools for biologically based activities such as genetic analysis, measurement of gene expression, and gene function determination. Develop software or applications for scientific or technical use.
Data Mining as A Service in Medical Devices
Data mining is the field of Computer Science that forms the basis for data analytics. As the term specifies, it is utilized for ‘mining’ or extracting the most significant data from ‘Big Data’ or massive data sets. It not only serves as an extraction tool but also assists manufacturers and researchers in deriving hypotheses or conclusions from the existing Big Data sets, which can be applied toward improving existing services. Even though Data Mining is extensively utilized in commercial domains such as e-commerce or finance, it is indeed also proving to be vital for improving services in the health care domain. When utilized for developing or enhancing medical services, manufacturers should make sure their mining tool is safe and qualified enough to be integrated with medical devices...
The Basic Facts About Biosimilars
In order to promote the uptake of biosimilars across the U.S., stakeholders must be informed about the basic science of these important medicines. Created out of the Biosimilars Forum's Biosimilars Roundtable (formerly the Biosimilars Forum Stakeholder Workshop group), this two page document incorporates feedback from nearly 40 stakeholder groups, from patient advocacy organizations to physician and payer groups, and presents the basic knowledge that all stakeholders need to know about the safety and efficacy of biosimilars, including answers to common myths about them.
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二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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Cyclothymia Test: Diagnosing, Symptoms, Treatment, and Impact | The Lifescien...The Lifesciences Magazine
The cyclothymia test is a pivotal tool in the diagnostic process. It helps clinicians assess the presence and severity of symptoms associated with cyclothymia.Faridkot ℂ𝕒𝕝𝕝 𝔾𝕚𝕣𝕝𝕤 7742996321 ℂ𝕒𝕝𝕝 𝔾𝕚𝕣𝕝𝕤 Faridkot
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How to effectively start market research in the health tech industry by defining objectives, crafting problem statements, selecting methods, identifying data collection sources, and setting clear timelines. This guide covers all the preliminary steps needed to lay a strong foundation for your research.
This lays foundation of scoping research project what are the
Before embarking on a research project, especially one aimed at scoping and defining parameters like the one described for health tech IT, several crucial considerations should be addressed. Here’s a comprehensive guide covering key aspects to ensure a well-structured and successful research initiative:
1. Define Research Objectives and Scope
Clear Objectives: Define specific goals such as understanding market needs, identifying new opportunities, assessing risks, or refining pricing strategies.
Scope Definition: Clearly outline the boundaries of the research in terms of geographical focus, target demographics (e.g., age, socio-economic status), and industry sectors (e.g., healthcare IT).
3. Review Existing Literature and Resources
Literature Review: Conduct a thorough review of existing research, market reports, and relevant literature to build foundational knowledge.
Gap Analysis: Identify gaps in existing knowledge or areas where further exploration is needed.
4. Select Research Methodology and Tools
Methodological Approach: Choose appropriate research methods such as surveys, interviews, focus groups, or data analytics.
Tools and Resources: Select tools like Google Forms for surveys, analytics platforms (e.g., SimilarWeb, Statista), and expert consultations.
5. Ethical Considerations and Compliance
Ethical Approval: Ensure compliance with ethical guidelines for research involving human subjects.
Data Privacy: Implement measures to protect participant confidentiality and adhere to data protection regulations (e.g., GDPR, HIPAA).
6. Budget and Resource Allocation
Resource Planning: Allocate resources including time, budget, and personnel required for each phase of the research.
Contingency Planning: Anticipate and plan for unforeseen challenges or adjustments to the research plan.
7. Develop Research Instruments
Survey Design: Create well-structured surveys using tools like Google Forms to gather quantitative data.
Interview and Focus Group Guides: Prepare detailed scripts and discussion points for qualitative data collection.
8. Sampling Strategy
Sampling Design: Define the sampling frame, size, and method (e.g., random sampling, stratified sampling) to ensure representation of target demographics.
Participant Recruitment: Plan recruitment strategies to reach and engage the intended participant groups effectively.
9. Data Collection and Analysis Plan
Data Collection: Implement methods for data gathering, ensuring consistency and validity.
Analysis Techniques: Decide on analytical approaches (e.g., statistical
Types of Cancer Treatments | Forms of cancer treatment
Cancer treatment has advanced significantly over the years, offering patients various options tailored to their specific type of cancer and stage of disease. Understanding the different types of cancer treatments can help patients make informed decisions about their care. In this ppt, we have listed most common forms of cancer treatment available today.
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NURSING MANAGEMENT OF PATIENT WITH EMPHYSEMA .PPT
Prepared by Prof. BLESSY THOMAS, VICE PRINCIPAL, FNCON, SPN.
Emphysema is a disease condition of respiratory system.
Emphysema is an abnormal permanent enlargement of the air spaces distal to terminal bronchioles, accompanied by destruction of their walls and without obvious fibrosis.
Emphysema of lung is defined as hyper inflation of the lung ais spaces due to obstruction of non respiratory bronchioles as due to loss of elasticity of alveoli.
It is a type of chronic obstructive
pulmonary disease.
It is a progressive disease of lungs.
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Significance of Analytics in Biosimilars
- 1. Significance of Analytics in Biosimilars By: Govind Yatnalkar Biosimilars are biological products that are composed or produced in living systems such as yeast or bacteria. They are defined as the medicinal drugs which function exactly like certain existing and approved drugs, often referred to as “reference product”. The major facets which make up a biosimilar are that they are “highly similar” to their reference product and they should have no clinically significant differences from safety, efficiency, and functionality perspectives.1 A vitaltool in the development,evaluation,andFDAapprovalofthese biosimilar products is data analytics. These data analyses are key evaluation measures for approving these biosimilars and may be performed using Cloud or AI-based statistical tools. A major chunk of the development phase is invested in the comparison of these biosimilars to their reference product. Initially, the analysis is subjected to the drug structure to validate if both products are similar in terms of chemical compositions. This includes comparing and analyzing chemical structural elements such as primary chemical sequences, molecule behavior, and bonding types. These structures also indirectly point to the functionality which is the next step in the analysis.2 The next step includes functionality comparisons which may be executed using the processes of pharmacokinetics and pharmacodynamics (PK-PD studies).3 It is the process of administrating a drug to record how the body handles a drug considering its life cycle which is the absorption, distribution, metabolism, and excretion. This analytical study is performed on the originator drug and then compared to clinical study results which were recorded while validating the reference product. Additional clinical studies including risk assessments may be performed to verify, validate, analyze, and identify any existing residual risks and potential hazards. If the FDA observes that the data recorded from all mentioned analyses supports the fact that the originator drug is highly similar to its reference product and possesses no clinically meaningful differences, the FDA approves the developed biosimilar to be further labeled and marketed in the health and medical industry. For analytics, hardware devices may also be utilized such as mass spectrometry systems which provide the behavior of protein molecules in 3-dimensional space. These may be coupled with Cloud or AI-based statistical algorithms such as Decision Trees or Neural Networks to log 1 FDA (February 2020). Biosimilars. Retrieved on November 29th from https://www.fda.gov/drugs/therapeutic- biologics-applications-bla/biosimilars. 2 FDA (May 2018). The Basics of Biosimilars.Retrieved on November 28th from https://www.youtube.com/watch?v=1s7W1EKUekk&ab_channel=U.S.FoodandDrugAdministration. 3 Chad Briscoe (October 2020) The Difference Between Pharmacokinetics and Pharmacodynamic. Retrieved on Nov ember 29th from https://www.bioagilytix.com/blog/2020/10/05/the-difference-between-pharmacokinetics-and- pharmacodynamics/
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