Future of Pharmacovigilance
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The document discusses the changing landscape and future of pharmacovigilance. It notes that automation, cognitive technologies, and advanced analytics are transforming pharmacovigilance from compiling data for regulators to creating a learning system that improves drug risk-benefit profiles and patient safety. It promotes ClinoSol Research as providing training on latest technologies in pharmacovigilance like artificial intelligence, data analytics, and risk management to help professionals analyze and interpret increasing data volumes in the field. The document outlines ClinoSol's services, trainers, collaborations, and positive student feedback regarding their practical, career-oriented pharmacovigilance training programs.
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Role of Clinical Data Management in Risk-Based Monitoring
Clinical Data Management (CDM) plays a significant role in the implementation of Risk-Based Monitoring (RBM) within clinical trials. RBM is an approach that focuses monitoring efforts on areas of highest risk, thereby optimizing resource allocation, enhancing data quality, and ensuring patient safety. Here's how CDM contributes to RBM
Integration of Clinical Trial Systems: Enhancing Collaboration and Efficiency
Clinical trials are complex endeavors that involve numerous stakeholders, vast datasets, and intricate processes. To streamline operations, enhance collaboration, and improve efficiency, the integration of clinical trial systems has emerged as a pivotal solution. This strategic approach involves connecting various components, such as electronic data capture (EDC) systems, clinical trial management systems (CTMS), and electronic health records (EHR), fostering a cohesive ecosystem that accelerates the pace of research and ensures data accuracy.
Decentralized clinical trials.pptx
A decentralized clinical trial (DCT) is an innovative approach to conducting clinical research that leverages digital technologies and remote processes to enable greater patient participation and data collection outside of traditional clinical trial settings. In a decentralized trial, participants have the flexibility to engage in the study from their own homes or local healthcare facilities, reducing the need for frequent visits to centralized trial sites.
The key features of decentralized clinical trials include:
Remote Participant Engagement: DCTs utilize various digital tools and platforms to facilitate participant engagement. This may involve virtual visits, telemedicine consultations, electronic informed consent, and remote monitoring of participants' health status and adherence to study protocols.
Mobile and Wearable Technologies: Mobile devices and wearable sensors play a significant role in decentralized trials. They enable real-time data collection, including vital signs, patient-reported outcomes, medication adherence, and other relevant health data. These technologies enhance convenience for participants while providing continuous, objective data for researchers.
Virtual Study Operations: DCTs employ virtual tools for study management and coordination. These include electronic data capture systems, online study training, remote monitoring of data quality, and electronic trial master files. Virtual platforms facilitate seamless communication between study teams, investigators, and participants.
Local Healthcare Providers: In decentralized trials, local healthcare providers or clinical research sites often play a more substantial role in participant recruitment, enrollment, and follow-up. These providers may be responsible for conducting study visits, collecting samples, and implementing study interventions, while ensuring compliance with the trial protocol.
Data Security and Privacy: Protecting participant data and maintaining privacy are critical in decentralized trials. Robust data security measures, such as encryption, secure data transmission, and compliance with privacy regulations like GDPR or HIPAA, are implemented to safeguard participants' sensitive information.
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The document outlines key success criteria and best practices for clinical commissioning groups under the new NHS framework. It discusses the need to design sustainable patient pathways through collaborative relationships. Commissioning groups will need to make intelligent use of data combined with GP experience to define "waste free" pathways and ensure compliance with guidelines. A multi-dimensional approach is recommended that involves needs assessment, evidence-based practice, service redesign, and choosing optimal treatment locations through modeling and trend analysis. Operational excellence is seen as integral to effectively implementing the new GP commissioning framework.
Data Governance and Compliance in Clinical Research
Data governance and compliance are paramount in clinical research to ensure the integrity, quality, and ethical conduct of studies. Proper data governance practices establish a framework for managing data throughout the research lifecycle, while compliance ensures adherence to regulations, guidelines, and ethical standards. Here's an overview of data governance and compliance in clinical research
Cloud-Based Solutions for Clinical Data Management
Cloud-based solutions have become increasingly popular in the field of clinical data management due to their scalability, accessibility, cost-effectiveness, and potential for collaboration. These solutions offer a range of benefits for managing and analyzing clinical data while ensuring security and compliance with regulatory requirements such as HIPAA. Here are some key cloud-based solutions for clinical data management
Risk Based Monitoring in Clinical trials_Aishwarya Janjale.pptx
Risk-Based Monitoring (RBM) in clinical trials represents a departure from traditional, one-size-fits-all monitoring approaches. This innovative strategy tailors monitoring activities to the specific risks associated with a trial, optimizing resource utilization and enhancing data quality. This article explores the key principles, benefits, and challenges of RBM, illustrating its transformative impact on the landscape of clinical trial oversight.
Key Principles:
Risk Identification and Assessment:
RBM begins with a comprehensive assessment of potential risks to data integrity, patient safety, and study endpoints. These risks are identified based on factors such as study complexity, patient population, and investigational product characteristics.
AGENDA CBI RBM 2019 | RISK-BASED TRIAL MANAGEMENT and MONITORING
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Will I see you in Philadelphia next week? In case you don’t already know, I’ve been invited to speak at CBI’s Risk-Based Trial Management and Monitoring Conference.
I’m going to be sharing real world, pragmatic guidance that you can implement immediately to effectively influence your clinical trial performance.
My presentation, Practical Usage of KRIs and QTLs in Clinical Trials, will take place next Thursday, November 14th at 9:45am. I’m going to share with you:
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Role of Clinical Data Management in Risk-Based Monitoring
Clinical Data Management (CDM) plays a significant role in the implementation of Risk-Based Monitoring (RBM) within clinical trials. RBM is an approach that focuses monitoring efforts on areas of highest risk, thereby optimizing resource allocation, enhancing data quality, and ensuring patient safety. Here's how CDM contributes to RBM
Integration of Clinical Trial Systems: Enhancing Collaboration and Efficiency
Clinical trials are complex endeavors that involve numerous stakeholders, vast datasets, and intricate processes. To streamline operations, enhance collaboration, and improve efficiency, the integration of clinical trial systems has emerged as a pivotal solution. This strategic approach involves connecting various components, such as electronic data capture (EDC) systems, clinical trial management systems (CTMS), and electronic health records (EHR), fostering a cohesive ecosystem that accelerates the pace of research and ensures data accuracy.
Decentralized clinical trials.pptx
A decentralized clinical trial (DCT) is an innovative approach to conducting clinical research that leverages digital technologies and remote processes to enable greater patient participation and data collection outside of traditional clinical trial settings. In a decentralized trial, participants have the flexibility to engage in the study from their own homes or local healthcare facilities, reducing the need for frequent visits to centralized trial sites.
The key features of decentralized clinical trials include:
Remote Participant Engagement: DCTs utilize various digital tools and platforms to facilitate participant engagement. This may involve virtual visits, telemedicine consultations, electronic informed consent, and remote monitoring of participants' health status and adherence to study protocols.
Mobile and Wearable Technologies: Mobile devices and wearable sensors play a significant role in decentralized trials. They enable real-time data collection, including vital signs, patient-reported outcomes, medication adherence, and other relevant health data. These technologies enhance convenience for participants while providing continuous, objective data for researchers.
Virtual Study Operations: DCTs employ virtual tools for study management and coordination. These include electronic data capture systems, online study training, remote monitoring of data quality, and electronic trial master files. Virtual platforms facilitate seamless communication between study teams, investigators, and participants.
Local Healthcare Providers: In decentralized trials, local healthcare providers or clinical research sites often play a more substantial role in participant recruitment, enrollment, and follow-up. These providers may be responsible for conducting study visits, collecting samples, and implementing study interventions, while ensuring compliance with the trial protocol.
Data Security and Privacy: Protecting participant data and maintaining privacy are critical in decentralized trials. Robust data security measures, such as encryption, secure data transmission, and compliance with privacy regulations like GDPR or HIPAA, are implemented to safeguard participants' sensitive information.
Gp commissioning kinetik
The document outlines key success criteria and best practices for clinical commissioning groups under the new NHS framework. It discusses the need to design sustainable patient pathways through collaborative relationships. Commissioning groups will need to make intelligent use of data combined with GP experience to define "waste free" pathways and ensure compliance with guidelines. A multi-dimensional approach is recommended that involves needs assessment, evidence-based practice, service redesign, and choosing optimal treatment locations through modeling and trend analysis. Operational excellence is seen as integral to effectively implementing the new GP commissioning framework.
Data Governance and Compliance in Clinical Research
Data governance and compliance are paramount in clinical research to ensure the integrity, quality, and ethical conduct of studies. Proper data governance practices establish a framework for managing data throughout the research lifecycle, while compliance ensures adherence to regulations, guidelines, and ethical standards. Here's an overview of data governance and compliance in clinical research
Cloud-Based Solutions for Clinical Data Management
Cloud-based solutions have become increasingly popular in the field of clinical data management due to their scalability, accessibility, cost-effectiveness, and potential for collaboration. These solutions offer a range of benefits for managing and analyzing clinical data while ensuring security and compliance with regulatory requirements such as HIPAA. Here are some key cloud-based solutions for clinical data management
Risk Based Monitoring in Clinical trials_Aishwarya Janjale.pptx
Risk-Based Monitoring (RBM) in clinical trials represents a departure from traditional, one-size-fits-all monitoring approaches. This innovative strategy tailors monitoring activities to the specific risks associated with a trial, optimizing resource utilization and enhancing data quality. This article explores the key principles, benefits, and challenges of RBM, illustrating its transformative impact on the landscape of clinical trial oversight.
Key Principles:
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Will I see you in Philadelphia next week? In case you don’t already know, I’ve been invited to speak at CBI’s Risk-Based Trial Management and Monitoring Conference.
I’m going to be sharing real world, pragmatic guidance that you can implement immediately to effectively influence your clinical trial performance.
My presentation, Practical Usage of KRIs and QTLs in Clinical Trials, will take place next Thursday, November 14th at 9:45am. I’m going to share with you:
• How to identify and close the gaps between risks and KRIs
• What the difference is between KRIs and QTLs, and how to use them effectively
• Useful examples of Centralized Monitoring findings from open data
• How to detect, combat and prevent fraud and sloppiness at an early stage
• How AI and ML advance risk-based approaches
So I can’t wait to see you at this informative and fun-filled industry expert forum,
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The Baldrige Award established in 1987 promotes quality awareness in US companies. It assesses manufacturing and service firms using 7 categories weighted towards results and customer satisfaction.
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Role of Clinical Data Management in Clinical Research
Clinical data management (CDM) plays a critical role in clinical research by ensuring the accuracy, completeness, and consistency of clinical trial data. Here are some key roles and responsibilities of CDM in clinical research:
Data collection: CDM is responsible for designing and implementing data collection procedures to ensure that all data is collected in a standardized and consistent manner.
Data quality control: CDM is responsible for implementing quality control procedures to ensure that data is accurate, complete, and consistent across all study sites.
Data cleaning: CDM is responsible for identifying and resolving data discrepancies or errors in the data that may impact the analysis of study results.
Data analysis: CDM is responsible for performing statistical analyses of the data collected in the clinical trial, which are used to evaluate the safety and efficacy of the investigational product.
Database management: CDM is responsible for developing and maintaining the study database, which is used to store and manage all data collected in the clinical trial.
Study documentation: CDM is responsible for ensuring that all study documentation is accurate, complete, and up-to-date, including study protocols, data collection forms, and standard operating procedures.
Compliance with regulatory requirements: CDM is responsible for ensuring that all data collected in the clinical trial is compliant with regulatory requirements and industry standards
NMaingi Abridged CV v.01012017AB
Nicodemus Maingi is the director of the HP-Strathmore Research Laboratory at Strathmore University in Nairobi, Kenya. He has over 10 years of experience leading projects in healthcare IT, including developing systems to support disease surveillance, supply chain management, and monitoring of health programs. Some of the major systems developed under his leadership include an electronic laboratory information management system, tools for early infant HIV diagnosis and treatment, and health commodity logistics platforms. He also has over 15 years of experience in academia as a lecturer, administrator, and research supervisor at Strathmore University.
Clinical Data Management importance in Clinical Trials
Clinical data management (CDM) is a crucial component of clinical trials that involves the collection, processing, validation, and management of data generated during the course of a clinical study. It plays a vital role in ensuring the accuracy, integrity, and quality of the data collected, which is essential for reliable analysis and interpretation of study results. Here are some key reasons highlighting the importance of clinical data management in clinical trials:
Data Integrity: CDM focuses on maintaining the integrity of the data collected during a clinical trial. This involves applying standardized data collection methods, implementing data validation checks, and ensuring data accuracy, completeness, and consistency. By maintaining data integrity, CDM helps minimize errors, reduce bias, and enhance the reliability and credibility of study findings.
Compliance with Regulatory Requirements: Clinical trials are subject to stringent regulatory guidelines and standards. CDM ensures that the collected data meets these requirements, including adherence to Good Clinical Practice (GCP) guidelines, data privacy regulations, and relevant industry-specific standards. Proper data management practices facilitate regulatory inspections and audits, demonstrating the compliance of the study with applicable regulations.
Efficient Data Handling: CDM streamlines the process of data collection, entry, and management. It involves the development of data collection forms (e.g., electronic Case Report Forms - eCRFs), database design, and data validation procedures. Efficient data handling processes minimize data discrepancies, reduce manual errors, and improve data quality, ultimately leading to more accurate and reliable study results.
Quality Control and Data Cleaning: CDM includes quality control measures to identify and resolve discrepancies, missing data, or inconsistencies in the collected data. Data cleaning activities involve cross-checking and resolving any discrepancies or outliers, ensuring data accuracy and completeness. This process enhances data quality, minimizing the potential impact of data errors on study outcomes.
Database Lock and Analysis: CDM plays a vital role in the database lock process, where the final study database is considered "locked" and no further data modifications are allowed. Database lock is a critical step before statistical analysis and reporting. Proper CDM practices ensure that the database is complete, accurate, and ready for analysis, enabling researchers to generate valid and meaningful results.
Data Security and Confidentiality: CDM incorporates measures to ensure the security and confidentiality of participant data. This includes implementing data access controls, data encryption, and adherence to data protection regulations. Protecting participant privacy and maintaining data security are crucial for building trust with study participants and complying with legal and ethical requirements.
Data Traceability and
Audit Trail
An audit trail in clinical research refers to a documented record of all activities and events related to the planning, conduct, and reporting of a clinical trial or research study. It provides a chronological and detailed account of actions taken, decisions made, and changes implemented throughout the course of the study. The primary purpose of maintaining an audit trail is to ensure the integrity, traceability, and transparency of clinical research data and processes. Here's a more in-depth look at the concept of an audit trail in clinical research:
Workflow of CDM
Clinical Data Management (CDM) is a critical component of clinical research that involves the collection, cleaning, validation, and management of clinical trial data to ensure its accuracy, integrity, and compliance with regulatory requirements. The workflow of CDM typically consists of several key stages, each with specific activities and processes. Here is an overview of the typical workflow of CDM:
Study Startup:
Protocol Review: CDM teams begin by reviewing the clinical trial protocol to understand the study's objectives, endpoints, data collection requirements, and timelines.
Database Design: Based on the protocol, the team designs a data capture system or electronic data capture (EDC) system. This includes creating data entry forms, defining data validation checks, and setting up data dictionaries.
Data Collection:
Case Report Form (CRF) Design: CDM professionals design electronic or paper CRFs to collect data during the trial. CRFs capture specific data points required by the protocol.
Data Entry: Data is entered into the CRFs, either electronically by site personnel or through paper CRFs.
Data Validation: CDM teams implement validation checks to ensure data quality and consistency. Data validation checks may include range checks, consistency checks, and logic checks.
Query Management: Queries are generated when data discrepancies or inconsistencies are identified. CDM teams send queries to investigational sites for resolution.
Data Cleaning and Quality Control:
Data Cleaning: Data are cleaned to resolve discrepancies, discrepancies, and inconsistencies. This involves querying data discrepancies with clinical trial sites.
Data Review: CDM teams review data to ensure completeness and accuracy, and any outstanding queries are resolved.
Quality Control: Quality control processes are applied to verify the integrity and accuracy of data.
Database Lock:
Once the data are cleaned, reviewed, and validated, the database is locked, indicating that no further changes can be made to the data. Database lock is a critical step before data analysis begins.
Data Export and Analysis:
Data is exported from the database and provided to biostatisticians and researchers for statistical analysis. This analysis is conducted to determine the study's outcomes, efficacy, and safety profile.
Data listings, summaries, and tables are generated for regulatory submissions, reports, and publications.
Final Study Reporting:
After data analysis, CDM teams contribute to the preparation of final study reports, which provide a comprehensive overview of the trial's results, data quality, and regulatory compliance.
Archiving and Documentation:
Clinical trial data, documentation, and databases are archived to ensure their long-term availability for regulatory audits and future reference.
Regulatory Submission: CDM teams provide support for regulatory submissions.
CLINICAL DATA MANAGEMENT- AN OVERVIEW
Clinical data management (CDM) is a process that involves the collection, cleaning, and management of data from clinical trials. The goal of CDM is to ensure that the data collected in a study is accurate, complete, and consistent, and that it complies with regulatory requirements and industry standards.
CDM involves several key activities, including:
Data collection: This involves collecting data from various sources, such as electronic medical records, case report forms, and laboratory results. The data must be collected in a standardized and consistent manner to ensure that it is reliable and can be used for analysis.
Data cleaning: This involves identifying and resolving errors or inconsistencies in the data. This may involve checking for missing or incomplete data, verifying the accuracy of the data, and resolving any discrepancies or inconsistencies.
Data validation: This involves verifying that the data meets regulatory requirements and industry standards. This includes checking that the data is consistent with the study protocol, that it has been collected and recorded accurately, and that it is complete and reliable.
Data management: This involves storing and managing the data in a secure and organized manner. The data must be protected from unauthorized access or disclosure and must be available for analysis and reporting.
Effective CDM is critical for ensuring the validity and reliability of clinical trial data. It helps to ensure that the data collected is accurate, complete, and consistent, which ultimately leads to more reliable study results. Additionally, effective CDM helps to ensure compliance with regulatory requirements and industry standards, which is essential for gaining regulatory approval for new medical interventions.
The Role of Technology in Streamlining Clinical Trial Processes
Technology plays a significant role in streamlining clinical trial processes, enhancing efficiency, data quality, and participant engagement. Here are some key areas where technology contributes to the optimization of clinical trials
Best Practices for Data Collection and Management in Clinical Trials
Data collection and management in clinical trials are crucial for ensuring the accuracy, integrity, and reliability of study findings. Here are some best practices for data collection and management in clinical trials:
Standardized Data Collection: Use standardized data collection tools, such as electronic case report forms (eCRFs), to ensure consistent and uniform data capture across study sites. Clearly define data fields, formats, and coding conventions to minimize variability and errors.
Training and Standard Operating Procedures (SOPs): Provide comprehensive training to study personnel on data collection procedures, including proper documentation, data entry, and quality control measures. Develop and implement SOPs that outline data collection, handling, and storage processes.
Source Data Verification (SDV): Perform regular source data verification to ensure the accuracy and completeness of data. Compare data entered in the eCRFs with original source documents (e.g., medical records, laboratory reports) to identify discrepancies and resolve any inconsistencies.
Data Quality Checks: Implement automated data quality checks to identify potential errors, outliers, and inconsistencies in the data. Range checks, logic checks, and consistency checks can help identify data entry errors or missing data points.
Data Security and Confidentiality: Implement robust data security measures to protect participant confidentiality and comply with data protection regulations. Use secure servers, encryption techniques, and access controls to prevent unauthorized access and ensure data privacy.
Data Monitoring and Audit Trails: Establish a data monitoring plan to regularly review and validate data for accuracy and completeness. Maintain an audit trail that tracks any changes made to the data, including the date, time, and reason for modifications.
Data Backup and Storage: Implement regular data backup procedures to prevent data loss. Store data securely and ensure appropriate backup storage to mitigate risks associated with data corruption or system failures.
Patient-Centric Clinical Trials: Empowering Participants and Enhancing Outcomes'
Patient-centric clinical trials prioritize the needs and perspectives of participants, aiming to empower them and enhance overall trial outcomes. By involving patients in the design, conduct, and evaluation of clinical trials, patient-centric approaches aim to improve recruitment and retention rates, increase patient engagement and satisfaction, and generate more meaningful and applicable results. Here are some key aspects of patient-centric clinical trials
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Pharmacovigilance Auditing: Ensuring Data Integrity and Compliance
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Key Objectives of Pharmacovigilance Auditing:
Data Integrity: Auditing aims to ensure the accuracy, completeness, and reliability of pharmacovigilance data. This includes confirming that adverse event reports are properly collected, documented, and stored, and that data manipulation or unauthorized alterations do not occur.
Regulatory Compliance: Audits help ensure that pharmacovigilance activities are in accordance with applicable regulations and guidelines, such as those set forth by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), the Food and Drug Administration (FDA), the European Medicines Agency (EMA), and other relevant health authorities.
Process Validation: Auditing assesses the effectiveness of pharmacovigilance processes, such as signal detection, risk assessment, and benefit-risk analysis. It helps verify that these processes are robust, consistently applied, and appropriately documented.
Quality Management System Evaluation: Auditing evaluates the overall quality management system of a pharmaceutical company, including organizational structure, roles and responsibilities, training programs, and documentation practices related to pharmacovigilance.
Vendor Audits: Companies often outsource certain pharmacovigilance activities to third-party vendors. Auditing these vendors ensures they meet the same high standards for data integrity and regulatory compliance.
Continuous Improvement: Auditing identifies areas for improvement within pharmacovigilance systems and processes. It provides valuable insights that can be used to enhance procedures, address deficiencies, and prevent future issues.
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Role of Drug Regulatory agencies in Clinical Research.
Drug regulatory agencies play a pivotal role in overseeing and regulating clinical research to ensure the safety, efficacy, and quality of pharmaceutical products. Their primary responsibility is to protect public health by evaluating the data generated from clinical trials and determining whether a drug can be approved for marketing and use in patients. Here are some key roles of drug regulatory agencies in clinical research:
Approval and Oversight of Clinical Trials: Regulatory agencies review and approve clinical trial protocols, ensuring that they adhere to ethical and scientific standards. They assess the design, methodology, and objectives of trials to ensure patient safety and the reliability of data generated.
Regulatory Guidance and Standards: These agencies provide guidance and establish regulations governing the conduct of clinical trials, including Good Clinical Practice (GCP) guidelines, which outline standards for trial design, conduct, monitoring, and reporting.
Review and Evaluation of Data: Regulatory agencies review the data collected from clinical trials to assess the safety and efficacy of investigational drugs. They evaluate study results, adverse events, and other relevant information to make informed decisions about drug approval.
Drug Approval and Labeling: Based on the evaluation of clinical trial data, regulatory agencies decide whether to approve a drug for marketing and use. They also determine the appropriate labeling, including indications, dosages, contraindications, and warnings, to ensure safe and effective use by healthcare professionals and patients.
Post-Marketing Surveillance: Regulatory agencies continue to monitor the safety and effectiveness of approved drugs through post-marketing surveillance programs. They collect and analyze real-world data on adverse events and drug utilization to identify potential risks and take appropriate regulatory actions if safety concerns arise.
Enforcement of Regulations: Regulatory agencies enforce compliance with regulatory requirements and take enforcement actions against sponsors, investigators, or manufacturers who fail to adhere to ethical or regulatory standards in clinical research.
International Collaboration: Many regulatory agencies collaborate with counterparts in other countries to harmonize regulatory standards, exchange information, and streamline the drug approval process, facilitating global drug development and access to new therapies.
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Data privacy and consent management are critical aspects of ensuring that individuals' personal information is handled responsibly and ethically, particularly in healthcare settings where sensitive medical data is involved. Data privacy refers to the protection of personal information from unauthorized access, use, or disclosure, while consent management involves obtaining and managing individuals' permissions for the collection, storage, and processing of their data.
In healthcare, patients entrust providers with their sensitive medical information, expecting that it will be kept confidential and used only for legitimate purposes related to their care. Robust data privacy measures include encryption, access controls, and anonymization techniques to safeguard patient data from unauthorized access or breaches. Additionally, healthcare organizations must adhere to regulatory standards such as HIPAA in the United States or GDPR in the European Union, which outline specific requirements for the protection of patient information and impose penalties for non-compliance.
Consent management plays a crucial role in ensuring that individuals have control over how their data is used. Patients should be informed about the purposes for which their data will be collected and processed, as well as any potential risks or benefits associated with its use. Obtaining informed consent involves providing individuals with clear and transparent information about their privacy rights and giving them the opportunity to consent to or decline the use of their data for specific purposes. Consent management systems help healthcare organizations track and manage patients' consent preferences, ensuring that data is used in accordance with their wishes and legal requirements.
Effective data privacy and consent management practices not only protect individuals' privacy rights but also foster trust and transparency in healthcare relationships. By implementing robust security measures, respecting patients' autonomy, and promoting informed decision-making, healthcare organizations can uphold the principles of data privacy and consent while leveraging data responsibly to improve patient care and outcomes.
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Data collection: CDM is responsible for designing and implementing data collection procedures to ensure that all data is collected in a standardized and consistent manner.
Data quality control: CDM is responsible for implementing quality control procedures to ensure that data is accurate, complete, and consistent across all study sites.
Data cleaning: CDM is responsible for identifying and resolving data discrepancies or errors in the data that may impact the analysis of study results.
Data analysis: CDM is responsible for performing statistical analyses of the data collected in the clinical trial, which are used to evaluate the safety and efficacy of the investigational product.
Database management: CDM is responsible for developing and maintaining the study database, which is used to store and manage all data collected in the clinical trial.
Study documentation: CDM is responsible for ensuring that all study documentation is accurate, complete, and up-to-date, including study protocols, data collection forms, and standard operating procedures.
Compliance with regulatory requirements: CDM is responsible for ensuring that all data collected in the clinical trial is compliant with regulatory requirements and industry standards
NMaingi Abridged CV v.01012017AB
Nicodemus Maingi is the director of the HP-Strathmore Research Laboratory at Strathmore University in Nairobi, Kenya. He has over 10 years of experience leading projects in healthcare IT, including developing systems to support disease surveillance, supply chain management, and monitoring of health programs. Some of the major systems developed under his leadership include an electronic laboratory information management system, tools for early infant HIV diagnosis and treatment, and health commodity logistics platforms. He also has over 15 years of experience in academia as a lecturer, administrator, and research supervisor at Strathmore University.
Clinical Data Management importance in Clinical Trials
Clinical data management (CDM) is a crucial component of clinical trials that involves the collection, processing, validation, and management of data generated during the course of a clinical study. It plays a vital role in ensuring the accuracy, integrity, and quality of the data collected, which is essential for reliable analysis and interpretation of study results. Here are some key reasons highlighting the importance of clinical data management in clinical trials:
Data Integrity: CDM focuses on maintaining the integrity of the data collected during a clinical trial. This involves applying standardized data collection methods, implementing data validation checks, and ensuring data accuracy, completeness, and consistency. By maintaining data integrity, CDM helps minimize errors, reduce bias, and enhance the reliability and credibility of study findings.
Compliance with Regulatory Requirements: Clinical trials are subject to stringent regulatory guidelines and standards. CDM ensures that the collected data meets these requirements, including adherence to Good Clinical Practice (GCP) guidelines, data privacy regulations, and relevant industry-specific standards. Proper data management practices facilitate regulatory inspections and audits, demonstrating the compliance of the study with applicable regulations.
Efficient Data Handling: CDM streamlines the process of data collection, entry, and management. It involves the development of data collection forms (e.g., electronic Case Report Forms - eCRFs), database design, and data validation procedures. Efficient data handling processes minimize data discrepancies, reduce manual errors, and improve data quality, ultimately leading to more accurate and reliable study results.
Quality Control and Data Cleaning: CDM includes quality control measures to identify and resolve discrepancies, missing data, or inconsistencies in the collected data. Data cleaning activities involve cross-checking and resolving any discrepancies or outliers, ensuring data accuracy and completeness. This process enhances data quality, minimizing the potential impact of data errors on study outcomes.
Database Lock and Analysis: CDM plays a vital role in the database lock process, where the final study database is considered "locked" and no further data modifications are allowed. Database lock is a critical step before statistical analysis and reporting. Proper CDM practices ensure that the database is complete, accurate, and ready for analysis, enabling researchers to generate valid and meaningful results.
Data Security and Confidentiality: CDM incorporates measures to ensure the security and confidentiality of participant data. This includes implementing data access controls, data encryption, and adherence to data protection regulations. Protecting participant privacy and maintaining data security are crucial for building trust with study participants and complying with legal and ethical requirements.
Data Traceability and
Audit Trail
An audit trail in clinical research refers to a documented record of all activities and events related to the planning, conduct, and reporting of a clinical trial or research study. It provides a chronological and detailed account of actions taken, decisions made, and changes implemented throughout the course of the study. The primary purpose of maintaining an audit trail is to ensure the integrity, traceability, and transparency of clinical research data and processes. Here's a more in-depth look at the concept of an audit trail in clinical research:
Workflow of CDM
Clinical Data Management (CDM) is a critical component of clinical research that involves the collection, cleaning, validation, and management of clinical trial data to ensure its accuracy, integrity, and compliance with regulatory requirements. The workflow of CDM typically consists of several key stages, each with specific activities and processes. Here is an overview of the typical workflow of CDM:
Study Startup:
Protocol Review: CDM teams begin by reviewing the clinical trial protocol to understand the study's objectives, endpoints, data collection requirements, and timelines.
Database Design: Based on the protocol, the team designs a data capture system or electronic data capture (EDC) system. This includes creating data entry forms, defining data validation checks, and setting up data dictionaries.
Data Collection:
Case Report Form (CRF) Design: CDM professionals design electronic or paper CRFs to collect data during the trial. CRFs capture specific data points required by the protocol.
Data Entry: Data is entered into the CRFs, either electronically by site personnel or through paper CRFs.
Data Validation: CDM teams implement validation checks to ensure data quality and consistency. Data validation checks may include range checks, consistency checks, and logic checks.
Query Management: Queries are generated when data discrepancies or inconsistencies are identified. CDM teams send queries to investigational sites for resolution.
Data Cleaning and Quality Control:
Data Cleaning: Data are cleaned to resolve discrepancies, discrepancies, and inconsistencies. This involves querying data discrepancies with clinical trial sites.
Data Review: CDM teams review data to ensure completeness and accuracy, and any outstanding queries are resolved.
Quality Control: Quality control processes are applied to verify the integrity and accuracy of data.
Database Lock:
Once the data are cleaned, reviewed, and validated, the database is locked, indicating that no further changes can be made to the data. Database lock is a critical step before data analysis begins.
Data Export and Analysis:
Data is exported from the database and provided to biostatisticians and researchers for statistical analysis. This analysis is conducted to determine the study's outcomes, efficacy, and safety profile.
Data listings, summaries, and tables are generated for regulatory submissions, reports, and publications.
Final Study Reporting:
After data analysis, CDM teams contribute to the preparation of final study reports, which provide a comprehensive overview of the trial's results, data quality, and regulatory compliance.
Archiving and Documentation:
Clinical trial data, documentation, and databases are archived to ensure their long-term availability for regulatory audits and future reference.
Regulatory Submission: CDM teams provide support for regulatory submissions.
CLINICAL DATA MANAGEMENT- AN OVERVIEW
Clinical data management (CDM) is a process that involves the collection, cleaning, and management of data from clinical trials. The goal of CDM is to ensure that the data collected in a study is accurate, complete, and consistent, and that it complies with regulatory requirements and industry standards.
CDM involves several key activities, including:
Data collection: This involves collecting data from various sources, such as electronic medical records, case report forms, and laboratory results. The data must be collected in a standardized and consistent manner to ensure that it is reliable and can be used for analysis.
Data cleaning: This involves identifying and resolving errors or inconsistencies in the data. This may involve checking for missing or incomplete data, verifying the accuracy of the data, and resolving any discrepancies or inconsistencies.
Data validation: This involves verifying that the data meets regulatory requirements and industry standards. This includes checking that the data is consistent with the study protocol, that it has been collected and recorded accurately, and that it is complete and reliable.
Data management: This involves storing and managing the data in a secure and organized manner. The data must be protected from unauthorized access or disclosure and must be available for analysis and reporting.
Effective CDM is critical for ensuring the validity and reliability of clinical trial data. It helps to ensure that the data collected is accurate, complete, and consistent, which ultimately leads to more reliable study results. Additionally, effective CDM helps to ensure compliance with regulatory requirements and industry standards, which is essential for gaining regulatory approval for new medical interventions.
The Role of Technology in Streamlining Clinical Trial Processes
Technology plays a significant role in streamlining clinical trial processes, enhancing efficiency, data quality, and participant engagement. Here are some key areas where technology contributes to the optimization of clinical trials
Best Practices for Data Collection and Management in Clinical Trials
Data collection and management in clinical trials are crucial for ensuring the accuracy, integrity, and reliability of study findings. Here are some best practices for data collection and management in clinical trials:
Standardized Data Collection: Use standardized data collection tools, such as electronic case report forms (eCRFs), to ensure consistent and uniform data capture across study sites. Clearly define data fields, formats, and coding conventions to minimize variability and errors.
Training and Standard Operating Procedures (SOPs): Provide comprehensive training to study personnel on data collection procedures, including proper documentation, data entry, and quality control measures. Develop and implement SOPs that outline data collection, handling, and storage processes.
Source Data Verification (SDV): Perform regular source data verification to ensure the accuracy and completeness of data. Compare data entered in the eCRFs with original source documents (e.g., medical records, laboratory reports) to identify discrepancies and resolve any inconsistencies.
Data Quality Checks: Implement automated data quality checks to identify potential errors, outliers, and inconsistencies in the data. Range checks, logic checks, and consistency checks can help identify data entry errors or missing data points.
Data Security and Confidentiality: Implement robust data security measures to protect participant confidentiality and comply with data protection regulations. Use secure servers, encryption techniques, and access controls to prevent unauthorized access and ensure data privacy.
Data Monitoring and Audit Trails: Establish a data monitoring plan to regularly review and validate data for accuracy and completeness. Maintain an audit trail that tracks any changes made to the data, including the date, time, and reason for modifications.
Data Backup and Storage: Implement regular data backup procedures to prevent data loss. Store data securely and ensure appropriate backup storage to mitigate risks associated with data corruption or system failures.
Patient-Centric Clinical Trials: Empowering Participants and Enhancing Outcomes'
Patient-centric clinical trials prioritize the needs and perspectives of participants, aiming to empower them and enhance overall trial outcomes. By involving patients in the design, conduct, and evaluation of clinical trials, patient-centric approaches aim to improve recruitment and retention rates, increase patient engagement and satisfaction, and generate more meaningful and applicable results. Here are some key aspects of patient-centric clinical trials
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Key Objectives of Pharmacovigilance Auditing:
Data Integrity: Auditing aims to ensure the accuracy, completeness, and reliability of pharmacovigilance data. This includes confirming that adverse event reports are properly collected, documented, and stored, and that data manipulation or unauthorized alterations do not occur.
Regulatory Compliance: Audits help ensure that pharmacovigilance activities are in accordance with applicable regulations and guidelines, such as those set forth by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), the Food and Drug Administration (FDA), the European Medicines Agency (EMA), and other relevant health authorities.
Process Validation: Auditing assesses the effectiveness of pharmacovigilance processes, such as signal detection, risk assessment, and benefit-risk analysis. It helps verify that these processes are robust, consistently applied, and appropriately documented.
Quality Management System Evaluation: Auditing evaluates the overall quality management system of a pharmaceutical company, including organizational structure, roles and responsibilities, training programs, and documentation practices related to pharmacovigilance.
Vendor Audits: Companies often outsource certain pharmacovigilance activities to third-party vendors. Auditing these vendors ensures they meet the same high standards for data integrity and regulatory compliance.
Continuous Improvement: Auditing identifies areas for improvement within pharmacovigilance systems and processes. It provides valuable insights that can be used to enhance procedures, address deficiencies, and prevent future issues.
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Role of Drug Regulatory agencies in Clinical Research.
Drug regulatory agencies play a pivotal role in overseeing and regulating clinical research to ensure the safety, efficacy, and quality of pharmaceutical products. Their primary responsibility is to protect public health by evaluating the data generated from clinical trials and determining whether a drug can be approved for marketing and use in patients. Here are some key roles of drug regulatory agencies in clinical research:
Approval and Oversight of Clinical Trials: Regulatory agencies review and approve clinical trial protocols, ensuring that they adhere to ethical and scientific standards. They assess the design, methodology, and objectives of trials to ensure patient safety and the reliability of data generated.
Regulatory Guidance and Standards: These agencies provide guidance and establish regulations governing the conduct of clinical trials, including Good Clinical Practice (GCP) guidelines, which outline standards for trial design, conduct, monitoring, and reporting.
Review and Evaluation of Data: Regulatory agencies review the data collected from clinical trials to assess the safety and efficacy of investigational drugs. They evaluate study results, adverse events, and other relevant information to make informed decisions about drug approval.
Drug Approval and Labeling: Based on the evaluation of clinical trial data, regulatory agencies decide whether to approve a drug for marketing and use. They also determine the appropriate labeling, including indications, dosages, contraindications, and warnings, to ensure safe and effective use by healthcare professionals and patients.
Post-Marketing Surveillance: Regulatory agencies continue to monitor the safety and effectiveness of approved drugs through post-marketing surveillance programs. They collect and analyze real-world data on adverse events and drug utilization to identify potential risks and take appropriate regulatory actions if safety concerns arise.
Enforcement of Regulations: Regulatory agencies enforce compliance with regulatory requirements and take enforcement actions against sponsors, investigators, or manufacturers who fail to adhere to ethical or regulatory standards in clinical research.
International Collaboration: Many regulatory agencies collaborate with counterparts in other countries to harmonize regulatory standards, exchange information, and streamline the drug approval process, facilitating global drug development and access to new therapies.
Data Privacy and consent management .. .
Data privacy and consent management are critical aspects of ensuring that individuals' personal information is handled responsibly and ethically, particularly in healthcare settings where sensitive medical data is involved. Data privacy refers to the protection of personal information from unauthorized access, use, or disclosure, while consent management involves obtaining and managing individuals' permissions for the collection, storage, and processing of their data.
In healthcare, patients entrust providers with their sensitive medical information, expecting that it will be kept confidential and used only for legitimate purposes related to their care. Robust data privacy measures include encryption, access controls, and anonymization techniques to safeguard patient data from unauthorized access or breaches. Additionally, healthcare organizations must adhere to regulatory standards such as HIPAA in the United States or GDPR in the European Union, which outline specific requirements for the protection of patient information and impose penalties for non-compliance.
Consent management plays a crucial role in ensuring that individuals have control over how their data is used. Patients should be informed about the purposes for which their data will be collected and processed, as well as any potential risks or benefits associated with its use. Obtaining informed consent involves providing individuals with clear and transparent information about their privacy rights and giving them the opportunity to consent to or decline the use of their data for specific purposes. Consent management systems help healthcare organizations track and manage patients' consent preferences, ensuring that data is used in accordance with their wishes and legal requirements.
Effective data privacy and consent management practices not only protect individuals' privacy rights but also foster trust and transparency in healthcare relationships. By implementing robust security measures, respecting patients' autonomy, and promoting informed decision-making, healthcare organizations can uphold the principles of data privacy and consent while leveraging data responsibly to improve patient care and outcomes.
Pharmacovigilance in Patriatics and Geriatrics
Pharmacovigilance in pediatrics and geriatrics is of paramount importance due to the unique physiological and pharmacokinetic characteristics of these populations. In pediatrics, factors such as rapid growth, organ maturation, and differences in drug metabolism can influence the safety and efficacy of medications. Therefore, monitoring for adverse drug reactions (ADRs), including both known and unexpected effects, is crucial in ensuring the safe use of drugs in children. Additionally, since pediatric patients often receive off-label or unlicensed medications due to limited clinical trials in this population, pharmacovigilance efforts play a critical role in gathering real-world data to inform pediatric drug use.
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Patient centered Pharmacovigilance .. .
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Collaborative initiatives involving patient advocacy groups play a crucial role in advancing the success of precision medicine. Precision medicine, also known as personalized medicine, tailors medical treatment and interventions to the characteristics of each patient, considering factors such as genetic makeup, lifestyle, and environment. Patient advocacy groups contribute to the success of precision medicine in several ways:
Raising Awareness and Education:
Patient advocacy groups are instrumental in raising awareness about precision medicine among their communities.
They provide education and resources to patients, caregivers, and the general public, promoting a better understanding of the benefits and implications of precision medicine.
Patient Empowerment:
Advocacy groups empower patients by providing them with information about their conditions and treatment options.
They help patients understand the importance of participating in precision medicine initiatives, including clinical trials and genetic testing.
Supporting Research and Development:
Patient advocacy groups often collaborate with researchers and industry stakeholders to support the development of targeted therapies and diagnostics.
By actively participating in research initiatives, advocacy groups contribute to the identification of genetic markers, biomarkers, and other factors that influence treatment response.
Influencing Policy and Regulation:
Advocacy groups advocate for policies that support the advancement of precision medicine.
They work to ensure that regulations promote patient access to personalized treatments and protect patient rights, privacy, and data security.
Fostering Collaboration:
Patient advocacy groups facilitate collaboration among patients, researchers, healthcare providers, and industry partners.
They create platforms for sharing information, experiences, and best practices, fostering a collaborative environment that accelerates progress in precision medicine.
Clinical Trial Recruitment:
Advocacy groups play a crucial role in recruiting patients for clinical trials related to precision medicine.
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Feedback and Patient-Centric Approaches:
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This patient-centric approach helps researchers and healthcare professionals tailor precision medicine strategies to better meet the needs and expectations of the individuals they serve.
Championing Access to Treatments:
Patient advocacy groups work to improve patient access to precision medicine treatments.
They advocate for affordable and equitable access to innovative therapies, ensuring that the benefits of precision medicine are accessible to a broader population.
In summary, patient advocacy groups play a multifaceted role in advancing precision medicine by fostering awareness,
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Osvaldo Bernardo Muchanga-GASTROINTESTINAL INFECTIONS AND GASTRITIS-2024.pdfOsvaldo Bernardo Muchanga
GASTROINTESTINAL INFECTIONS AND GASTRITIS
Osvaldo Bernardo Muchanga
Gastrointestinal Infections
GASTROINTESTINAL INFECTIONS result from the ingestion of pathogens that cause infections at the level of this tract, generally being transmitted by food, water and hands contaminated by microorganisms such as E. coli, Salmonella, Shigella, Vibrio cholerae, Campylobacter, Staphylococcus, Rotavirus among others that are generally contained in feces, thus configuring a FECAL-ORAL type of transmission.
Among the factors that lead to the occurrence of gastrointestinal infections are the hygienic and sanitary deficiencies that characterize our markets and other places where raw or cooked food is sold, poor environmental sanitation in communities, deficiencies in water treatment (or in the process of its plumbing), risky hygienic-sanitary habits (not washing hands after major and/or minor needs), among others.
These are generally consequences (signs and symptoms) resulting from gastrointestinal infections: diarrhea, vomiting, fever and malaise, among others.
The treatment consists of replacing lost liquids and electrolytes (drinking drinking water and other recommended liquids, including consumption of juicy fruits such as papayas, apples, pears, among others that contain water in their composition).
To prevent this, it is necessary to promote health education, improve the hygienic-sanitary conditions of markets and communities in general as a way of promoting, preserving and prolonging PUBLIC HEALTH.
Gastritis and Gastric Health
Gastric Health is one of the most relevant concerns in human health, with gastrointestinal infections being among the main illnesses that affect humans.
Among gastric problems, we have GASTRITIS AND GASTRIC ULCERS as the main public health problems. Gastritis and gastric ulcers normally result from inflammation and corrosion of the walls of the stomach (gastric mucosa) and are generally associated (caused) by the bacterium Helicobacter pylor, which, according to the literature, this bacterium settles on these walls (of the stomach) and starts to release urease that ends up altering the normal pH of the stomach (acid), which leads to inflammation and corrosion of the mucous membranes and consequent gastritis or ulcers, respectively.
In addition to bacterial infections, gastritis and gastric ulcers are associated with several factors, with emphasis on prolonged fasting, chemical substances including drugs, alcohol, foods with strong seasonings including chilli, which ends up causing inflammation of the stomach walls and/or corrosion. of the same, resulting in the appearance of wounds and consequent gastritis or ulcers, respectively.
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CLASSIFICATION OF H1 ANTIHISTAMINICS-
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1)HIGHLY SEDATIVE-DIPHENHYDRAMINE,DIMENHYDRINATE,PROMETHAZINE,HYDROXYZINE 2)MODERATELY SEDATIVE- PHENARIMINE,CYPROHEPTADINE, MECLIZINE,CINNARIZINE
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TRIPROLIDINE,CLEMASTINE
SECOND GENERATION ANTIHISTAMINICS-FEXOFENADINE,
LORATADINE,DESLORATADINE,CETIRIZINE,LEVOCETIRIZINE,
AZELASTINE,MIZOLASTINE,EBASTINE,RUPATADINE. Mechanism of action of 2nd generation antihistaminics-
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histamine at the H1 receptors.
Pharmacological actions-
Antagonism of histamine-The H1 antagonists effectively block histamine induced bronchoconstriction, contraction of intestinal and other smooth muscle and triple response especially wheal, flare and itch. Constriction of larger blood vessel by histamine is also antagonized.
2) Antiallergic actions-Many manifestations of immediate hypersensitivity (type I reactions)are suppressed. Urticaria, itching and angioedema are well controlled.3) CNS action-The older antihistamines produce variable degree of CNS depression.But in case of 2nd gen antihistaminics there is less CNS depressant property as these cross BBB to significantly lesser extent.
4) Anticholinergic action- many H1 blockers
in addition antagonize muscarinic actions of ACh. BUT IN 2ND gen histaminics there is Higher H1 selectivitiy : no anticholinergic side effects
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Academy of Oriental Medicine at Austin
Academy of Oriental Medicine at Austin
Academy of Oriental Medicine at Austin
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Osvaldo Bernardo Muchanga-GASTROINTESTINAL INFECTIONS AND GASTRITIS-2024.pdf
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Future of Pharmacovigilance
- 1. Welcome The Changing Landscape & Future of Pharmacovigilance Above and Beyond Presenter: C.S. Mujeebuddin Founder and CEO ClinoSol Research Pvt. Ltd. 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 1
- 2. Introduction • Automation, cognitive technologies, and advanced analytics are providing opportunities to transform pharmacovigilance from the process of compiling data and preparing information for regulators to creating a learning system to improve a drug’s risk-benefit profile, help providers select the optimal treatment, and increase product quality and patient safety. 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 2
- 3. The Industry Drivers of New-Era PV • Growing AE Volumes with Disease Burden and Complexity • More Complex Global Regulations • Increasing Complexity in Product Portfolios and Turn Arounds • Rising Costs and Organizational Revenue Cycle Management • Wearables and Social Media Driven Real-world Data • Regulator’s Evaluating for RWE Sources • Evolution of Data Science and Machine Learning in Signal Detection and Case Management • Increasing Consumerism and Patient Centricity 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 3
- 4. The Current, Emerging and Futuristic Areas of PV Development 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 4
- 5. Capability of next-gen PV system for improved patient safety • Cognitive case processing to automate data intake and processing to help significantly improve the efficiency and quality of the AE life cycle. • Aggregate and operational reporting to accommodate high case volumes and large data sets. • Signal detection, evaluation, and management that consolidate and streamline processes and systems so analysts can perform validation and assessment activities and capture results and annotations without leaving the system, leading to more efficient, accurate signal management. • Safety metrics that leverage existing safety data, new real-world sources, and supervised and unsupervised machine learning to detect, assess, understand, and help prevent safety-related issues while uncovering benefits that can improve patient outcomes. 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 5
- 6. Why Advanced Learning and Certification are Essential for Your Career? • Designed to provide participants with a comprehensive understanding of the latest technologies in the field of pharmacovigilance. • Covers advanced topics such as artificial intelligence, data analytics, and risk management. By enrolling in this program, participants can learn about the latest tools and technologies used in pharmacovigilance and gain practical experience in applying these technologies in real-world scenarios. • Designed to help participants develop the skills needed to analyze and interpret data, identify trends, and make informed decisions. With the increasing volume of data generated in pharmacovigilance, it is essential to have the skills to manage and analyze this data effectively. The program provides participants with the opportunity to learn about data management, data mining, and data visualization techniques that are essential for making informed decisions and succeed in this dynamic field, and it can help them advance your careers in pharmacovigilance. 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 6
- 7. Who Are We? 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 7
- 8. What We Do? 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 8
- 9. Our Trainers 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 9
- 10. Our Services - Training 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 10
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- 12. Our Services – Resource Consulting 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 12 • 2019 – 75 • 2020 – 125 • 2021 – 378 • 2022 – 178 • 2023 – 35 (Jan-Current
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- 15. Our Collaborations 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 15 Benefits of Our Collaborations to Students: • National and International Industry Conferences • Real-time and Tool-based Live Workshops • Sponsored conference participation and certificates that adds value to your resume • Discounted Student Memberships and One-Year Learning Validity Exclusively for ClinoSol Students
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- 18. Thank You.. Any Questions!? 02-05-2023 www.clinosol.com | info@clinosol.com | 9121151622 18