San Diego Jobs’ Post

#hiring *Head of Clinical Operations (Sr. Director VP)*, San Diego, *United States*, fulltime #jobs #jobseekers #careers #SanDiegojobs #Californiajobs #Administration *Apply*: https://lnkd.in/deZBQDwY We started Genesis Therapeutics in 2019 with origins in field-leading AI + biophysics research. Since then, we've dramatically accelerated innovation in Machine Learning and molecular simulation technologies for drug discovery, with the goal of providing new treatments for patients with severe diseases. We are a mission-driven group of researchers, engineers, and drug developers. We believe close-knit, interdisciplinary collaboration is crucial for inventing important new technology and rapidly advancing optimal therapies. Genesis Therapeutics is seeking a Head of Clinical Operations to play a key role in delivering high-quality clinical results across multiple programs. The individual in this position will have the unique opportunity to grow and shape the Genesis Therapeutics clinical operation team at a rapidly growing biotechnology company. In addition, this individual will develop a new project team within the clinical organization to ensure Genesis Therapeutics is on the cutting edge of clinical innovation. This position will contribute to Genesis Therapeutics success by leading the clinical operations activities while working closely with other functional area leads at Genesis Therapeutics and third-party CROs and vendors. ResponsibilitiesThis is a high-profile management positionOversee the overall execution of the assigned clinical program(s) with a focus on quality, budget and timelines, including making decisions or recommending operational strategies in support of achieving Phase III clinical program objectivesLeads the strategy and tactics to successfully work with vendors, investigators, and cross-functional departments to develop, implement and deliver clinical studies/programs supporting our Phase 1-III programsChampions, promotes, and supports the development of high-quality, hard-working study teams to deliver outstanding clinical trialsHas broad experience selecting, managing, troubleshooting, and negotiating with CROs and supporting vendorsLeads strategies for patient recruitment and retention in clinical trialsEstablishes performance, quality, business efficiency, and innovation metrics for the clinical teams and vendors in collaboration with Quality and Risk Management; seeks to improve quality across study programs and ensures quality processes are followedMaintains awareness of industry trends and developments to help define the future strategic direction for the clinical programsPrepares and delivers training, both internally and externally, on new objectives or mandates from authorities, and on training associated with the clinical trials (Investigator Meetings, Re-training)Oversees and is accountable for program budgets, staffing, and tim

Harnessing AI and ML in Pharmacology and Psychopharmacology The integration of AI and ML in the field of pharmacology and psychopharmacology presents a transformative approach to drug discovery, drug repurposing, and understanding drug-drug interactions. In the context of drug discovery, AI and ML can streamline the process by predicting potential drug targets and simulating clinical trials. A well-developed Python script can process and analyze pharmacokinetic data, focusing on factors such as half-life, bioavailability, and drug interactions. This can significantly reduce the time and resources required to bring a new drug to market. Drug repurposing, or finding new uses for existing drugs, can also benefit from AI and ML. By analyzing large datasets, these technologies can identify potential drug-disease associations and predict drug responses based on patient data. This can lead to new treatment options without the need for extensive and costly clinical trials. Understanding drug-drug interactions is crucial in pharmacology. AI and ML can help predict these interactions by analyzing the metabolic pathways and pharmacokinetics of drugs. This can lead to safer and more effective treatment regimens. Tools like AlphaFold can be used to develop a drug-drug interaction discovery app. By integrating these tools with other services and datasets, we can enhance the app's predictive accuracy and user experience. However, the application of AI and ML in pharmacology is not without challenges. These include the integration of diverse data sources, the interpretability of AI and ML models, and the need for simplified explanations for a lay audience. By addressing these challenges, we can maximize the benefits of AI and ML in pharmacology and psychopharmacology. In conclusion, the integration of AI and ML in pharmacology and psychopharmacology can revolutionize drug discovery, drug repurposing, and our understanding of drug-drug interactions. By harnessing these technologies, we can bring safer and more effective treatments to patients more quickly and efficiently.

There’s a crisis in drug discovery that clinical developers don’t always know about...and it could be setting clinical trials up to fail. According to a Nature survey, 70% of researchers have failed to reproduce another scientist's experiments, prompting drug discovery insiders to declare a ‘Reproducibility Crisis’ in biomedical research. Part 2 of 2 (see Part 1 in comments 👇) 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗜𝗺𝗽𝗮𝗰𝘁 😬 We could be wasting time and money on a therapeutic that can’t and won’t work. Equally, we could kill a therapeutic that actually works, but we didn’t ask the right questions of it because we were focused in the wrong place. With 𝗼𝘃𝗲𝗿 𝟱𝟬% 𝗼𝗳 𝗣𝗵𝗮𝘀𝗲 𝗜𝗜 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀 𝗱𝘂𝗲 𝘁𝗼 𝗹𝗮𝗰𝗸 𝗼𝗳 𝗲𝗳𝗳𝗶𝗰𝗮𝗰𝘆, this is a multi-billion dollar dilemma that affects all of us in drug development, not just discovery and translational med folks. And while it would be unrealistic to expect basic research to translate 100% of the time, we should ask ourselves if our clinical hit rate would improve if we more robustly validated the basic research claims our product relies on before and during our development journey. According to a former Merck CMO, it takes on average “𝘢𝘱𝘱𝘳𝘰𝘹𝘪𝘮𝘢𝘵𝘦𝘭𝘺 𝘵𝘸𝘰 𝘵𝘰 𝘴𝘪𝘹 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘧𝘪𝘤 𝘱𝘦𝘳𝘴𝘰𝘯𝘯𝘦𝘭 𝘰𝘯𝘦 𝘵𝘰 𝘵𝘸𝘰 𝘺𝘦𝘢𝘳𝘴 𝘰𝘧 𝘸𝘰𝘳𝘬 𝘪𝘯 𝘢𝘯 𝘪𝘯𝘥𝘶𝘴𝘵𝘳𝘺 𝘭𝘢𝘣𝘰𝘳𝘢𝘵𝘰𝘳𝘺” to try to reproduce original experiments at an average cost of $500,000 to $2 million (1). Yet this spend only validates the preclinical, which we know doesn’t automatically translate into humans. 🔑 The key is to find meaningful, targeted ways to enhance our early-stage trials to bottom out biology cause and effect (beyond the often-ineffective addition of exploratory endpoints that aren’t sample-sized sufficiently to read out conclusively). Early validation in humans would mean laser-focused next-step trials, with biomarker, phenotype-breadth, and endpoint tyres well-kicked. 𝗧𝗮𝗸𝗲 𝗵𝗼𝗺𝗲 1️⃣ The next time you see a cure-is-in-sight-claiming headline, take it with a massive grain of salt. Resist being seduced by single ‘break through’ studies and always look for multiple follow-ups that replicate the original claims, not just build on them. 2️⃣ Approach AI-generated target candidates with caution if based primarily on published studies. 3️⃣ If you’re an investor, don’t be dazzled by prominent names and journals. Look at the validation efforts of the company. 4️⃣ And lastly, recognise that early-stage clinical trials will need to validate the biological pathway’s role in clinical expression every bit as much as the product’s efficacy if we want to better de-risk late-stage trial failures. Ultimately, it means more early-stage clinical testing as even when basic research is replicated, it doesn’t guarantee those results will translate into human. Only testing in humans does that. #clinicaltrials #clinicalresearch

Cell therapy startups face a whole host of challenges when seeking to translate their novel research into early-stage clinical trials. Setting up compliant manufacturing facilities and navigating regulatory requirements presents another layer of hurdles in terms of time, cost, and complexity — especially for small companies just entering this field. Our latest podcast unpacks these challenges and puts forth some guidance to help startups overcome them, optimize their resources, and advance their promising therapies to patients as efficiently as possible in the initial clinical stages. The FDA Group's CEO, Nicholas Capman, sits down with Mukesh Kumar, PhD, RAC, for a deep dive into the challenges of setting up #GMP facilities for cell therapy startups. Discussion points include: 🔹 Common questions startups have around engaging with the FDA and determining costs and timelines for GMP facility setup and clinical trials. 🔹 Recommendations for engaging regulatory experts early and doing first-in-human manufacturing in-house rather than through a CMO to reduce costs and time. (Talk to us about accessing these resources via staff augmentation.) 🔹 The importance of educating oneself on regulations before meetings with experts or the FDA to get the most benefit from those interactions. 🔹 Best practices for GMP facility setup and clinical trials in a cost-effective manner to advance cell therapies to patients. ——— Dr. Mukesh Kumar is CEO and Founder of FDAMap, a Washington DC-based firm helping manufacturers and developers of FDA-regulated products in regulatory affairs, quality assurance, clinical trials, and smart development strategies. His key expertise is in global regulatory project management, regulatory submissions, compliance inspections, operational management, supply management, clinical operations, and multi-national project management for medicinal and diagnostic products. He has led the clinical development of over 100 products over the last 20 years. He has been a leader in more than 150 clinical trials in about 34 countries, including countries in the EU, Taiwan, Korea, Japan, China, Canada, countries in South America, Australia, and India. He has led more than 100 GCP, GLP, GMP, and GACP audits in the US and several countries in Europe, North and South Americas, and Asia in the last 15 years. He has conducted numerous training workshops on FDA compliance-related issues and has authored numerous articles in peer-reviewed journals. 𝘞𝘢𝘵𝘤𝘩 𝘵𝘩𝘦 𝘧𝘪𝘳𝘴𝘵 𝘧𝘦𝘸 𝘮𝘪𝘯𝘶𝘵𝘦𝘴 𝘣𝘦𝘭𝘰𝘸 𝘢𝘯𝘥 𝘸𝘢𝘵𝘤𝘩 𝘰𝘳 𝘭𝘪𝘴𝘵𝘦𝘯 𝘵𝘰 𝘵𝘩𝘦 𝘧𝘶𝘭𝘭 𝘦𝘱𝘪𝘴𝘰𝘥𝘦 𝘰𝘯 𝘠𝘰𝘶𝘛𝘶𝘣𝘦 𝘰𝘳 𝘺𝘰𝘶𝘳 𝘱𝘳𝘦𝘧𝘦𝘳𝘳𝘦𝘥 𝘱𝘰𝘥𝘤𝘢𝘴𝘵 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘶𝘴𝘪𝘯𝘨 𝘵𝘩𝘦 𝘭𝘪𝘯𝘬𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴 𝘣𝘦𝘭𝘰𝘸. ⤵ #celltherapy #cellandgenetherapy #cgmp #fda #pharmaceuticals #clinicaltrials #clinicalresearch

AI and ML in Pharmacology The integration of AI and ML in the field of pharmacology and psychopharmacology presents a transformative approach to drug discovery, drug repurposing, and understanding drug-drug interactions. In the context of drug discovery, AI and ML can streamline the process by predicting potential drug targets and simulating clinical trials. A well-developed Python script can process and analyze pharmacokinetic data, focusing on factors such as half-life, bioavailability, and drug interactions. This can significantly reduce the time and resources required to bring a new drug to market. Drug repurposing, or finding new uses for existing drugs, can also benefit from AI and ML. By analyzing large datasets, these technologies can identify potential drug-disease associations and predict drug responses based on patient data. This can lead to new treatment options without the need for extensive and costly clinical trials. Understanding drug-drug interactions is crucial in pharmacology. AI and ML can help predict these interactions by analyzing the metabolic pathways and pharmacokinetics of drugs. This can lead to safer and more effective treatment regimens. Tools like AlphaFold can be used to develop a drug-drug interaction discovery app. By integrating these tools with other services and datasets, we can enhance the app's predictive accuracy and user experience. However, the application of AI and ML in pharmacology is not without challenges. These include the integration of diverse data sources, the interpretability of AI and ML models, and the need for simplified explanations for a lay audience. By addressing these challenges, we can maximize the benefits of AI and ML in pharmacology and psychopharmacology. In conclusion, the integration of AI and ML in pharmacology and psychopharmacology can revolutionize drug discovery, drug repurposing, and our understanding of drug-drug interactions. By harnessing these technologies, we can bring safer and more effective treatments to patients more quickly and efficiently.

AI and ML Psychopharmacology The integration of AI and ML in the field of pharmacology and psychopharmacology presents a transformative approach to drug discovery, drug repurposing, and understanding drug-drug interactions. In the context of drug discovery, AI and ML can streamline the process by predicting potential drug targets and simulating clinical trials. A well-developed Python script can process and analyze pharmacokinetic data, focusing on factors such as half-life, bioavailability, and drug interactions. This can significantly reduce the time and resources required to bring a new drug to market. Drug repurposing, or finding new uses for existing drugs, can also benefit from AI and ML. By analyzing large datasets, these technologies can identify potential drug-disease associations and predict drug responses based on patient data. This can lead to new treatment options without the need for extensive and costly clinical trials. Understanding drug-drug interactions is crucial in pharmacology. AI and ML can help predict these interactions by analyzing the metabolic pathways and pharmacokinetics of drugs. This can lead to safer and more effective treatment regimens. Tools like AlphaFold can be used to develop a drug-drug interaction discovery app. By integrating these tools with other services and datasets, we can enhance the app's predictive accuracy and user experience. However, the application of AI and ML in pharmacology is not without challenges. These include the integration of diverse data sources, the interpretability of AI and ML models, and the need for simplified explanations for a lay audience. By addressing these challenges, we can maximize the benefits of AI and ML in pharmacology and psychopharmacology. In conclusion, the integration of AI and ML in pharmacology and psychopharmacology can revolutionize drug discovery, drug repurposing, and our understanding of drug-drug interactions. By harnessing these technologies, we can bring safer and more effective treatments to patients more quickly and efficiently.

Harnessing AI and ML in Pharmacology and Psychopharmacology The integration of AI and ML in the field of pharmacology and psychopharmacology presents a transformative approach to drug discovery, drug repurposing, and understanding drug-drug interactions. In the context of drug discovery, AI and ML can streamline the process by predicting potential drug targets and simulating clinical trials. A well-developed Python script can process and analyze pharmacokinetic data, focusing on factors such as half-life, bioavailability, and drug interactions. This can significantly reduce the time and resources required to bring a new drug to market. Drug repurposing, or finding new uses for existing drugs, can also benefit from AI and ML. By analyzing large datasets, these technologies can identify potential drug-disease associations and predict drug responses based on patient data. This can lead to new treatment options without the need for extensive and costly clinical trials. Understanding drug-drug interactions is crucial in pharmacology. AI and ML can help predict these interactions by analyzing the metabolic pathways and pharmacokinetics of drugs. This can lead to safer and more effective treatment regimens. Tools like AlphaFold can be used to develop a drug-drug interaction discovery app. By integrating these tools with other services and datasets, we can enhance the app's predictive accuracy and user experience. However, the application of AI and ML in pharmacology is not without challenges. These include the integration of diverse data sources, the interpretability of AI and ML models, and the need for simplified explanations for a lay audience. By addressing these challenges, we can maximize the benefits of AI and ML in pharmacology and psychopharmacology.

There’s a crisis in drug discovery that clinical developers don’t always know about...and it could be setting clinical trials up to fail. According to a Nature survey, 70% of researchers have failed to reproduce another scientist's experiments, prompting drug discovery insiders to declare a ‘Reproducibility Crisis’ in biomedical research. Many of us in clinical development don’t fully understand how our drug discovery colleagues came up with the new drug we are trialling. That’s a shame, because if more of us knew about the Crisis our colleagues are facing, we might design our early-stage clinical studies to more thoroughly validate not only the new product’s efficacy, but also the biology that underpins its Mode of Action (MoA). And that could go a long way to minimising costly late-stage failures. 𝗧𝗵𝗲 𝗜𝘀𝘀𝘂𝗲 𝗶𝗻 𝗮 𝗡𝘂𝘁𝘀𝗵𝗲𝗹𝗹 🥜 1️⃣ Academics primarily conduct the basic research that reveals which biological pathways and molecules are involved in a disease’s regulation and expression.                                                                 2️⃣ These academics publish their finding in scientific journals.                                                                 3️⃣ Pharma and biotech drug discoverers scour said journals for pathways to target that have therapeutic (and market) potential. But... in 2011 & 12, Bayer and Amgen respectively published papers that revealed publication in a journal was no guarantee the paper’s findings would be reliable: 😞 "Often, key data could not be reproduced" wrote Khusru Asadullah, Vice President and head of target discovery at Bayer HealthCare in Berlin, in an article entitled, "Believe it or not" 😞  "The scientific community assumes that the claims in a preclinical study can be taken at face value," Begley and Ellis of MD Anderson Cancer Center wrote in Nature. “Unfortunately, this is not always the case." Many landmark studies scrutinised were published in prestigious journals and have been cited by hundreds to more than a thousand follow-up articles, leading to widespread acceptance of the conclusions when the foundation science itself may be suspect. Root Cause is multi-faceted. Lots of initiatives exist to tackle the likely culprits of confirmation bias, pressure to publish and win grants, careerism, poor training of students, journals that don’t review reports rigorously enough (to name a few). 👀 Tomorrow at 7:30am (GMT) I will be posting Part 2 about the Impact this has on the industry 👀. P.S. Do you agree (Y/N)? P.P.S. 👀 in comments 👇 for links to referenced articles. #clinicaltrials #clinicalresearch

Artificial intelligence cannot replace humans, but makes their work more accurate and easy. Drug Discovery and Development: AI algorithms are being used to analyze large datasets, such as genomic data, chemical structures, and clinical trial data, to identify potential drug targets, predict drug efficacy, and optimize drug development processes. AI-powered drug discovery platforms can accelerate the identification and validation of new drug candidates, reducing time and costs associated with traditional drug discovery methods. Patient Recruitment and Retention: AI-powered algorithms can analyze electronic health records (EHRs), patient data, and social media data to identify eligible patients for clinical trials, predict patient recruitment rates, and personalize recruitment strategies. Additionally, AI-driven engagement platforms can improve patient retention by providing personalized reminders, support, and incentives to trial participants. Clinical Trial Design and Optimization: AI algorithms can optimize clinical trial design by analyzing historical trial data, identifying relevant patient subpopulations, and predicting trial outcomes. AI-powered trial simulation platforms can simulate trial scenarios, optimize trial protocols, and predict the likelihood of trial success, helping sponsors make informed decisions and minimize risks. Real-Time Data Monitoring and Analysis: AI-powered platforms can monitor and analyze real-time clinical trial data, including patient-reported outcomes, biomarker data, and adverse events, to detect trends, anomalies, and safety signals. AI algorithms can identify potential safety issues early, enabling proactive intervention and decision-making by trial sponsors and regulatory authorities. Predictive Analytics and Risk Management: AI algorithms can analyze clinical trial data to predict patient outcomes, treatment responses, and disease progression, helping clinicians and researchers tailor treatment strategies and manage patient risk more effectively. AI-powered risk prediction models can identify high-risk patients, optimize treatment regimens, and improve clinical decision-making in real-time. Medical Imaging and Diagnostics: AI algorithms are being used to analyze medical imaging data, such as MRI scans, CT scans, and pathology slides, to assist in disease diagnosis, treatment planning, and patient monitoring. AI-powered image analysis platforms can detect abnormalities, quantify disease progression, and assist radiologists and pathologists in interpreting complex medical images more accurately. Regulatory Compliance and Pharmacovigilance: AI-powered platforms can streamline regulatory compliance processes, such as document management, regulatory submissions, and pharmacovigilance reporting. AI algorithms can analyze regulatory requirements, identify compliance gaps, and automate documentation tasks, reducing administrative burden and ensuring timely compliance with regulatory standards.

A preface on clinical research Clinical research is a branch of medical research . As drugs and other medical procedures we utilise is the most risk involved subject, it is necessary to ensure that it's very safe and efficient to use. As human body is a complex system the drugs we consume shouldn't produce any untoward effects. Clinical research mainly focuses to determine the safety and efficacy of medications,devices, diagnostic products and treatment regimen. This study is conducted on a group of people to determine their safety and efficacy. Clinical research is a broad term, and refers to the entire process of studying and writing about a drug, medical device, or a form of treatment . Clinical research is typically conducted in several phases which involves: Preclinical research - In prior to testing in humans, laboratory and animal studies are conducted to study their toxicity and pharmacokinetic profile. Phase 0 -exploratory investigational new drug studies These trial involves testing in small group of people with limited dose of the drug to get data on pharmacokinetic and pharmacodynamic parameters. Phase 1- safety and dosage It is conducted in a small group of 20 to 100 healthy volunteers or patients and helps to determine the drugs safety ,a safe dosage range and identify its side effects. Phase 2- Efficacy and side effects It is done on a large group of 100-300 patients to evaluate the drugs efficacy and safety . Phase 3- efficacy and monitoring of adverse reactions Done on a group of 1000 -3000 patients Phase 4- post marketing surveillance This comes into action after the drug has been approved for public use. Thus we conclude that by undergoing preclinical studies and multiple phases of human trial, we can necessarily bring out the potentiality of a drug and improve human health.