Emerging health problems Flashcards
Impact of climate change on health, anti-microbial resistance, drug resistant tuberculosis (29 cards)
How does climate change impact vector-borne diseases?
Eg malaria and dengue
Rising temps mean mosquitos can live in new places (expanded geographical range), and inrease transmission seasons or breeding cycles (eg for plasmodium)
Eg. Dengue fever and chickungunyah reported in new countries, previously it was limited to only tropical countries (WHO reporting intense dengue transmission in 2023)
Eg. In Pakistan 2022-2023 had increased malaria rates, flooding/river surges from glacier melting and extra rainfall meant extra standing water (mosquitos favorite)
How does misinformation and anti-science pose a threat to the future of health care?
Vaccines cause autism: lead anti-vaccine campaign which lead to increase in measles and whooping cough
Distrust in science and healthcare: may delay treatment and worsen outcomes, or abandon treatments, eg diet retreats for cancer
Anti-science misinformation delayed COVID-19 preventative measures
HIV misinformation can increase transmission and worsen treatments
What are potential solutions for combatting drug resistant TB?
Increase/develop better rapid diagnostic tools: eg GeneXpert and loop-mediated isothermal amplification (LAMP) rapidly detects TB and its resistance to first-line drugs (rifampicin), but expansion to second-line drugs (fluoroquinolones and kanamycin) is needed
New drugs/strategies: bedaquiline is a new and shorter drug, but combination therapies and new drugs need developing
Vaccine development: BCG is only vaccine, but not effective in adult TB
Increased prevention: transmission control (eg contact tracing), affordable second line drugs, decrease antibiotic misuse, increased education, increased monitoring and surveillance
International cooperation: increase funding, awareness, research collaboration through WHO, The Global Fund, and Stop TB Partnership
How has drug resistance impacted TB treatment?
Multidrug-resistant TB (MDR-TB): resistant to rifampicin and isoniazid
Extensively drug-resistant TB (XDR-TB): also resistant to fluoroquinolones and second line injectable drugs (eg kanamycin)
How has drug resistance impacted global TB rate?
- The World Health Organization (WHO) estimates that approximately 3.3% of new TB cases and 18% of previously treated cases globally have MDR-TB
- Might have derailed the WHO’s End TB Strategy (reduce TB deaths by 90% and new TB cases by 80% by 2030)
- Increased mortality and morbidity (esp in countries with poor healthcare infastructure and high rates of HIV)
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Slowed the decrease in overall TB incidence in countries w high rates of drug-resistant TB, the longer people live with TB the more likely transmission in
-** Increased costs for treating TB**
Countries in Eastern Europe, Central Asia, sub-Saharan Africa, and parts of South Asia have some of the highest rates of drug-resistant TB, and these regions have seen slower declines in overall TB incidence compared to areas with predominantly drug-sensitive TB
What are solutions in dealing with anti-microbial resistance?
Microbes= bacteria, viruses, fungi, or parasites
1) Limit antibiotic misuse through education or policy, or improving rapid diagnostic tests and vaccination
2) Regulate antibiotics in agriculture
3) Make new antibiotics
4) Make alternative treatments (bacteriophage therapy, antimicrobial pepties, vaccines)
(companies need incentives; fast-track approval process, economic benefits?)
How does increased globalisation impact human health?
Positive impacts:
- increased information being shared globally
- global health initiatives (Global Fund to fight AIS, TB, and Malaria)
Negative impacts:
- spread of infectious disease (COVID, Zika) or foodborne disease
- increased antimicrobial resistance
- exacerbate urbanization and health inequities
- inc climate change
The overuse and misuse of antibiotics in healthcare, agriculture, and livestock farming worldwide have exacerbated AMR
How might advances in technology (like AI) impact future health care?
- Diagnosis: identify at-risk patients for earlier diagnosis and treatment, analyze medical images, or interpret complex genomic data
- Personalised medicine: use AI for genomics/predict drug efficacy/interactions for tailored treatment for an individual
- Drug development: speed up the screening process and optimize clinical trial designs
- Robotic surgeries, could be powered by AI in the future and increase prescion
- Wearable devices can increase monitoring to improve quality of life and increase access to clinical trials
- Telemedicine was used during the pandemic, and good for remote care (improving access to healthcare)
What role does AI have in biomedical sciences?
- Can analyze images (X-rays, MRIs, CT scans, pathology slides) for quicker diagnosis or screening
- Can analyze and interpret scRNA-seq or WGS data
- Can identify drugs for repurposing or new drug targets
- Can predict protein structure (eg AlphaFold) or drug-protein interactions
What are potential solutions in dealing with anti-microbial resistance?
- Develop new antibiotics: can use AI and genomics in drug discovery
- Alternative therapies: phage therapy, monoclonal antibodies, Probiotics or microbiome-based therapies, Antivirulence agents
- Increased surveillance and diagnostics: genomic sequencing to monitor strains, GLASS, diagnostics to identify resistant strains
- Reduce misuse and strengthen prevention: promote vaccination, stop using antibiotics for virus
- Perserve current antibiotics: develop adjuvuncts like β-lactamase inhibitors, use narrow-spectrum antibiotics
How will AI be used in developing therapies for emerging health problems?
- Rapid Target Identification
- Accelerated Drug Discovery & Repurposing
- Personalised Therapies for Rare or Novel Conditions
- Combating Antimicrobial Resistance (AMR)
- Designing Next-Generation Therapies
How might AI be used in the fight against antibiotic and drug resistance?
- Screen for potential antibiotics: eg Halicin: An antibiotic discovered by MIT using deep learning, effective against multiple drug-resistant bacteria—including Acinetobacter baumannii, a WHO critical priority pathogen.
- Screens for drug repurposing: eg chlorpromazine (an antipsychotic) to target bacterial efflux pumps, a common resistance mechanism
- Screens sequencing/PCR data for resistance: eg DeepARG or ARIBA predict antibiotic resistance directly from metagenomic data in hours rather than dayse
- Surveillance: eg AI models trained on CDC/NHS data can flag emerging hotspots of MRSA or XDR-TB.
What advances in technology have contributed to Next-Gen medicine?
- Digital health technology (eg Wearable technology)
- Machine learning/ AI
What is precision medicine?
Uses genetic profiling, bio-markers, AI, pharmacogenomics, personalised therapeutics to develop targetted diagnosis/prevention/treatment to the specific characterisitics of a patient, maximising efficacy and minimizing adverse effects
Eg Trastuzumab (Herceptin) for HER2-positive breast cancer
Eg Specific SSRI dosing for people with CYP2D6 or CYP2C19
Eg Custom antisense oligonucleotides (Milasen for Batten disease)
How might digital health technology contribute to next-gen/ patient centric medicine?
- Wearable devices (in oncology, or to smart watches/mobile hotoplethysmogram to measure atrial fibrillation)
- Digital endpoints( eg AI used to track Parkinsons progression nocturnal breathing sounds, or smartwatch used to track cognitive decline in Alzheimers)
- Remote data aquisition (eg smartwatch monitors to evaluate drug effect in people with sickle-cell anemia)
- Decentralised trials: Virtual clinical trials and hospital-at-home, AI can improve patient experience (optimise notifications, enhance medical app user experience)
However, in addition to this data, multiple levels of data such as genomic, proteomic and genotype–phenotype-based clinical data and disease-specific measurements are needed to standardise these measurements for clinical trial endpoints
Endpoints are measures of health and/or disease and serve different purposes depending on the phase of the trial
How is AI used in next-generation medicine?
AI in healthcare:
- neurology: tracking disease evolution of amyotrophic lateral sclerosis and Parkinson’s disease
- cardiology: interpreting electrocardiograms (EKGs)
- cancer: used for diagnosis, and interpreting mamography or lung cancer screenings
AI in clinical trials:
- augment human intellegence, need for standardisation and transparant reporting (CONSORT-AI and SPIRIT-AI are published guidelines for reporting AI in clinical trials)
What is the Orphanet database?
Centralised resource of rare disease and orphan drug information, was created to improve diagnosis, care and treatment of patients w rare-diseases
Can accelerate diagnosises, and aid healthcare planning and epidemiology
What is the relevance of rare diseases?
1) Global health impact: although they are rare (1 in 2000) but cumulatilvely affect 300 mill people globally,
2) Healthcare burden: many go undiagnosed bc lack of access to genetic testing, and thus adequate treatment is delayed, and diagnosis and treatment is really expensive
3) Personalised medicine: developing novel treatments (gene therapy, antisense oligonucleotides, protein modulators, RNA-based therapeutics) for rare diseases drives development of personalised medicine and technology, which benefits everyone
4) Increase understanding of complex disease: progeria informs aging-related pathways, FXS or Rett syndrome informs autism related pathways, rare ovarian clear cell carcinoma first identified ARID1A as a tummor supressor gene, now known to be involved in many other cancers
ARID1A, a chromatin remodeling gene
orphan disease describes a rare disease whose rarity results in little or no funding or research for treatments, without financial incentives from governments or other agencies-> lead to Orphan Drug Act and Fast-track approval process which were applied during the pandemic
RNA-based therapeutics
were first trialed in rare diseases and are now used in broader clinical contexts (e.g., cancer, infectious disease
How is AI based deep precision learning used?
Enables targeted diagnostics, treatment predictions, and risk assessments to support precision medicine
- Predicting drug response from a patient’s genome (personalized medicine).
- Identifying early Alzheimers via MRI data
- Predict molecular binding or toxicity in developing drugs
- Modeling protein folding or mutation impact using deep neural networks.
What are umbrella clinical trials?
Test multiple different therapies for one disease/condition
eg: I-SPY breast cancer trial (patient-centered drug development using biomarkers and neoadjuvants)
eg: Lung-MAP (biomarker-driven protocol for previously treated squamous non-small-cell lung cancer)
I-SPY administers investigational treatments before surgery (neoadjuvant therapy), which allows for direct assessment of the tumor’s response to the therapy and helps in evaluating the treatment’s efficacy more quickly
What are issues with current drug development landscape?
- Biotech companies are struggling to get funding to transition from research to clinical trials
- Not enough investors because of finacial risk and low average returns
- Super expensive to get drug out of clinical trial (2.8 bill) and long time (10-15 years average) because of low phase 3 success rate
What is a basket/bucket trial?
Targetted therapy based on multiple disease types that all have same underlying molecular problem. They are tissue-agnostic or histology-independent studies.
Eg: Vemurafenib used to treat BRAFV600-mutant non-melanoma cancers (26 different ones) with 33% efficacy
What is a platform study?
Multi-arm, flexible designed trial that has one shared control arm, can add treatment groups during the trial, and has no defined end date
Eg: Randomised Evaluation of COVID-19 Thereapy (RECOVERY) showed dexamethasone is effective and hydroxychloroquine was ineffective
Eg: plasmaMATCH was initially for cancer/infectious disease drug development, then an arm was added for clinical pyschology and neurology
The UK Plasma Based Molecular Profiling of Advanced Breast Cancer to Inform Therapeutic CHoices (plasmaMATCH)
What is MOT study?
Master Observational Trial
Observational study that is broad and accepts patients independent of biomarkers and collects their data. Combination of biomarker-based interventional protocals with real-world data
eg: Registry of Oncology Outcomes Associated with Testing and Treatment (ROOT)
The MOT provides a clinical venue to allow molecular medicine to rapidly advance, answers questions that traditional interventional trials generally do not address, and seamlessly integrates with interventional trials in both diagnostic and therapeutic arenas. The result is a more comprehensive data collection ecosystem in precision medicine.
