El reciente incidente cibernético que involucró a la cadena de farmacias canadiense London Drugs fue realmente un completo accidente. Secuestro de datos La empresa confirmó que el ataque provocó el robo de datos confidenciales y una gran demanda de rescate.
En un comunicado presentado a RegistroLa empresa afirmó haber sido atacada, pero también confirmó que no tenía intención de pagar el rescate solicitado.
London Drugs sufrió un ciberataque a finales de abril de 2024, y eso fue Tuvo que cerrar temporalmente sus tiendas. en todo el oeste de Canadá luego de lo que describió en ese momento como un “problema operativo”.
LockBit ataca de nuevo
“Los farmacéuticos están preparados para satisfacer las necesidades farmacéuticas urgentes”, afirmó la empresa en ese momento. “Se recomienda a los clientes que se comuniquen con la farmacia de su tienda local para hacer arreglos”. La empresa tiene su sede en Richmond, Canadá, y opera al menos 78 tiendas en todo el país.
Un mes después, el “problema operativo” se convirtió en “un ataque orquestado por un sofisticado grupo de ciberdelincuentes globales”.
Más tarde se confirmó que este grupo era LockBit, uno de los operadores de ransomware más grandes del mundo. Supuestamente exigió 25 millones de dólares por la clave de descifrado y para mantener la privacidad de los datos robados. El grupo también dijo que London Drugs está dispuesto a pagar 8 millones de dólares para eliminar el problema.
Sin embargo, London Drugs dijo Registro “No quiere ni puede pagar un rescate a estos ciberdelincuentes”.
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LockBit parece haber robado archivos de la compañía London Drugs, que incluyen información de los empleados. La compañía dijo que los clientes no deberían verse afectados. Se desconocen los detalles sobre el tipo y la cantidad de datos, pero London Drugs los entregó gratis a sus empleados durante dos años Protección contra robo de identidad Y Servicios de seguimiento de crédito.
“Como se indicó anteriormente, aún no tenemos indicios de ninguna violación de las bases de datos de pacientes o clientes; y las bases de datos de nuestros empleados esenciales no parecen estar comprometidas. Si esto cambia a medida que continúa la investigación, notificaremos a las personas afectadas de acuerdo con la privacidad. leyes.” Concluyó el comunicado.
Wegovy, Ozempic and similar weight-loss drugs have become some of the most popular medications in the world. But legions of people are also quitting them. About two-thirds of those in the United States who started taking a drug of this class, known as GLP-1 agonists, in 2021 had stopped using them within a year, according to an industry analysis.
Researchers and clinicians often view GLP-1 agonists as lifelong treatments. But myriad factors can force individuals off the medications. People might lose the means to pay for the costly drugs, experience brutal side effects, be affected by continuing shortages or be offered limited-term prescriptions. The UK National Health Service (NHS), for instance, provides only two years of coverage for people taking the drugs for weight loss.
As the number of people with obesity continues to rise — the World Health Organization estimates that more than one billion people, or one-eighth of the global population, now have obesity — researchers have been answering a few key questions about what happens when people stop taking these medications for weight management.
What happens to weight and health when people quit?
Ozempic and Wegovy are both brand names for the drug semaglutide, which has been prescribed for several years to treat type 2 diabetes (Ozempic) and, since 2021, to those who are overweight or have obesity (Wegovy). The treatment’s aim is to reduce the risk of health complications posed by a large amount of excess body fat, such as heart and liver disease and certain cancers. The drug curbs hunger and food intake by mimicking a hormone, released by the gut after eating, that affects brain regions involved in appetite and reward.
Research has shown what happens when people stop taking GLP-1 agonists. Many regain a substantial amount of what they lost with the help of the medications. The body naturally tries to stay around its own weight point, a pull that obesity specialist Arya Sharma likens to a taut rubber band.
If you take a medication to alter your biology, “the tension of the rubber band is a lot less”, he explains. “But when I take away the medication, that tension is going to come back,” says Sharma, who is based in Berlin and consults part-time for several companies that have an interest in obesity.
For instance, in a trial that studied the effects of withdrawing from the drug, about 800 participants were given weekly injections of semaglutide — as well as making dietary changes, doing exercise and receiving counselling — and lost, on average, 10.6% of their body weight in about 4 months1. Then, one-third of the participants were switched to placebo injections for nearly a year. Eleven months after the switch, those on the placebo had regained almost 7% of their body weight, whereas participants who kept taking semaglutide continued to lose weight. Similarly, participants in an extended semaglutide trial, who lost an average of 17.3% of their body weight after more than one year of receiving the drug and making lifestyle changes, regained about two-thirds of that lost weight after one year without any clinical-trial interventions2.
And an observational study posted in January found that of nearly 20,300 people in the United States and Lebanon who lost at least 2.3 kilograms using semaglutide and who later stopped taking the drug, 44% regained at least 25% of their lost weight after one year (see go.nature.com/3u7nxmj). The work was posted by Epic Research, a journal based in Verona, Wisconsin, that uses an electronic health-record database to rapidly share medical knowledge. The study has not been peer reviewed.
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But weight wasn’t the only health risk factor that rebounded. In the withdrawal study1, those taking semaglutide beyond four months continued to lower their waist circumferences, says physician-scientist Fatima Cody Stanford at Massachusetts General Hospital and Harvard Medical School in Boston, who consults for several companies developing anti-obesity medications. But “those switched to placebo start to regain in that central region, which is around the key organs where we develop issues like fatty liver disease”, she says.
Other metabolic problems, such as heart disease and insulin resistance, are also linked to excess body fat in the midsection: a larger waistline usually means excess visceral fat, which wraps around organs deep in the abdominal cavity and is more metabolically active than fat that sits under the skin. These health risks, too, can revert to previous levels once the drug is stopped. People who came off semaglutide in clinical trials1,2 often saw a rebound in blood pressure and levels of blood glucose and cholesterol, which had improved while on the medication. (However, some of those measures remained better than those of clinical-trial participants who had never received semaglutide.)
Some people who have lowered their weight with the medication can maintain their new physique through diet and exercise alone, says Sharma. However, these individuals are at high risk of weight rebound if they return to old habits or undergo a stressful situation, he adds.
Still, researchers say, it’s important to acknowledge that not everyone responds to GLP-1 agonists. In one clinical trial, nearly 14% of participants did not lose a clinically meaningful amount of body weight — at least 5% — after more than one year of taking semaglutide3. Some health guidelines recommend stopping the treatment if that threshold has not been met after taking the medication for a few months.
What’s making people stop?
It can be hard to keep taking the medications. Some people experience side effects, such as nausea, vomiting, diarrhoea and constipation, that are so extreme that they have to quit. Almost 75% of participants taking semaglutide in the aforementioned clinical trial3 experienced gastrointestinal distress, although most instances were considered mild to moderate. About 7% of participants on the drug quit the trial because of adverse events, gastrointestinal or otherwise.
The drug’s manufacturer, Novo Nordisk in Bagsværd, Denmark, has also had trouble keeping up with the demand for semaglutide. Since 2022, the company has announced shortages of both Wegovy and Ozempic; the latter is sometimes prescribed off-label for weight loss.
Some people lose health-insurance coverage for the drugs, leaving them the choice of paying pricey premiums or stopping the treatment, says clinician-scientist Jamy Ard at Wake Forest University School of Medicine in Winston–Salem, North Carolina, who consults for and receives research funding from several companies that have obesity-drug-related programmes. Some of his patients, who paid for the drugs through their private health insurance, could no longer afford them when they retired and switched to the standard US federal health insurance for people aged 65 or older, which does not cover anti-obesity medications for weight-loss management. In the United States, Wegovy’s list price is US$1,350 for one month’s supply.
In some places, the availability of Wegovy and Ozempic has at times lagged behind demand.Credit: Carsten Snejbjerg/Bloomberg via Getty
And in the United Kingdom, where Wegovy was launched last September, those relying on the NHS for semaglutide treatment face a two-year time limit. Guidance issued last March by the National Institute for Health and Care Excellence (NICE) states that the time constraint comes from a lack of evidence for long-term use and limited access to specialized weight-management services.
That two-year rule “doesn’t make any clinical sense”, says clinician-researcher Alex Miras at Ulster University’s Derry–Londonderry Campus, UK, who receives research and financial support from several companies with an interest in obesity. But he acknowledges that the NICE decision came from cost-effectiveness calculations and the data the decision committee had at the time.
Semaglutide and other anti-obesity medications are available as NHS-funded treatments only at a tier of weight-care management that often requires hospital support and that typically lasts for only two years. Although Miras suspects that most doctors will abide by the time limit on semaglutide use, either to adhere strictly to NICE guidance or because of health-service capacity issues, some might make exceptions depending on disease severity.
Still, he urges the NHS to alter the system so that these medications are available at a lower tier of weight-management services, making the drugs accessible to community-level clinics and lowering the costs for the NHS.
As information about the use of GLP-1 agonists continues to come in, Miras hopes to see “changing policy and changing practice based on our learnings”.
What’s the best way to quit?
Treatment with a GLP-1 agonist requires starting with the smallest dose and gradually increasing the dosage over a few months. This escalating-dose schedule helps to minimize side effects. And, although physicians consider these drugs a lifelong treatment, there’s no biological harm in suddenly stopping.
“There’s not a withdrawal issue or anything like that, like other medications where you have to titrate off,” Stanford says. She advises people to inform their health-care providers of discontinued treatment so that they can keep medical records up to date.
But Ard has encountered anecdotal evidence that suggests otherwise. After quitting GLP-1 agonists, some people have reported higher levels of hunger than before they started treatment. Slowly tapering off the medication, rather than an abrupt stop, he says, “might help with decreasing that sense of rebound hunger”.
The ‘breakthrough’ obesity drugs that have stunned researchers
Sharma also recommends monitoring appetite and weight regained for those who willingly stop the drugs. “Don’t wait till you put 30 pounds back on,” he says. “If you stop and you regain five pounds, maybe that’s when you’ve got to jump back in.” Restarting the medication after time off does require working your way up from the smallest dose again, he says.
For people who have to stop taking GLP-1 agonists for the foreseeable future, continued dietary changes, exercise and mental-health counselling — which should already be in place while on the medication — are a must, Stanford says. People can also try anti-obesity medications that work in other ways, such as orlistat, which reduces how much dietary fat gets absorbed by the body.
But “by the time we’ve gotten to the GLPs, we’ve often unfortunately tried a lot of those”, Stanford says. Another option might be bariatric surgery.
One of the most common reasons that people stop taking their medications is that their weight plateaus, Sharma says, leading them to think that the drugs no longer work. He says that each person will respond to a dose in a different way, and that the dosage might need to be increased to lose more weight.
And many people want to stop once they have reached their goal weight, Ard says. Crossing that finish line gives a sense of completion, he says, especially because weight journeys celebrate milestones. But obesity is a chronic disease that requires lifelong maintenance, including medication, just like high blood pressure or heart disease do. “All we’ve done is modify their physiology,” he notes. “We haven’t cured the disease.”
So much work has gone into developing GLP-1 agonists and getting the medications to people who need them, Ard says. But “we need just as much — if not more — work to be done on what happens after people reach that goal in that weight-reduced state for the rest of their lives”
Genetic differences between individuals can affect how they respond to drugs.Credit: Jekesai Njikizana/AFP/Getty
How a person will respond to a drug is, in part, determined by their genetics. Africa holds the world’s most genetically diverse human population, and the United Nations estimates that, by 2050, the continent will be home to nearly 25% of the world’s people. Yet pharmacogenomics research — studies of how genetic variation plays into drug responses — is sorely lacking in African populations.
Less than 5% of the data in the pharmaco-genomics database PharmGKB are from African populations1. And of more than 300 drugs for which the US Food and Drug Administration provides pharmacogenetic advice, only 15 have been studied in African groups2.
Artificial intelligence (AI) can help to close the gap. AI models trained to identify pharmaco-genetic variants — DNA mutations that might affect how a drug acts — are emerging in many countries in the global north. But a dearth of genetic data for African populations, along with a lack of training and infrastructure, is holding up the use of such models in Africa. Here, we outline ways to overcome these hurdles.
Africa needs pharmacogenetics
Pharmacogenetic data have two key purposes. First, they can be used to select the best drugs for an individual person — for example, people with a pharmacogenetic mutation in an immune-response gene called HLA-B are hypersensitive to the antiviral drug abacavir, and should therefore be prescribed alternatives3. Second, such information can be used to refine the dose of existing drugs. For instance, a mutation in the gene CYP2C9, which encodes a cytochrome P450 enzyme involved in drug metabolism, results in reduced breakdown of the commonly used blood thinner warfarin. People who have this variant should be given a lower dose of the drug to prevent a build-up of unmetabolized warfarin in the body that would increase the risk of a haemorrhage4.
AI can help to speed up drug discovery — but only if we give it the right data
But pharmacogenetic information generated in the global north is not always relevant to African populations, because genetic variants are found at varying frequencies in different ethnogeographical groups. Research into variants that specifically affect drug responses in Africans living in Africa, and the African diaspora, is essential for several reasons.
First, a reliance on clinical data from the global north can put the health of Africans at risk. Take efavirenz. This promising HIV/AIDS drug was used successfully in the United States and Europe before being launched as a first-line treatment in Zimbabwe in 2015. But the dosing recommendations for Zimbabweans did not take into account that Africans are more likely than Europeans and Americans to carry a mutation in the gene CYP2B6. This mutation is associated with a range of side effects5. Whereas dizziness, irritability or headache were commonly reported side effects in European and US patients6, many people in Zimbabwe experienced hallucinations, anxiety and suicidal ideation when taking efavirenz7.
A researcher analyses results at a tuberculosis laboratory in Cotonou, Benin.Credit: Yanick Folly/AFP/Getty
Second, the effects of pharmacogenetic variants need to be considered alongside several other factors that play out differently in Africa compared with other world regions. For instance, each year, the 2.5 million people who contract tuberculosis in Africa are typically given the antimicrobial drug rifampicin, among other treatments. But rifampicin speeds up the body’s ability to metabolize drugs8, so if a person is taking medications for other conditions, their dosages will probably need to be modified. Lifestyle factors such as diet, which varies between populations, also affect drug metabolism, by altering the community of microorganisms in a person’s gut, which in turn affects how the body processes a drug. Research conducted outside Africa will probably fail to factor in these complexities.
Third, there are commercial incentives. The African population, coupled with the diaspora, represents a huge market share for drugs. Dosing and prescription adjustments to make drugs safe for these populations is likely to increase uptake, and so boost profits for global pharmaceutical companies.
AI on the horizon for Africa
Pharmacogenetic variants are hard to find — it can take vast swathes of clinical and genetic data to pinpoint a variant that is associated with a change in drug response. AI models are moving the field forwards by scouring the scientific literature for drug–gene connections that humans have missed.
Could Africa be the future for genomics research?
To identify more variants, the next step — building on large language models such as GPT-4 and LLaMA — is to train ‘foundation’ AI models that bring together several types of data for analysis. For pharmacogenetics, this information will include large-scale genetics resources such as biobanks; electronic health records containing medical text and treatment responses; clinical-trial reports and drug labels that capture adverse drug reactions and prescription recommendations; and in vivo and in vitro data about genetics and drug activity from the existing scientific literature.
Foundation models are already being developed for clinical science — for example, to identify biomarkers of cancer in imaging data9. We expect that an open-access foundation model with applications in pharmacogenetics will be available in the next year or two.
These foundation models will be biased towards countries in the global north, because their training data will come mainly from people of European descent (see ‘Data bias’). But researchers in Africa can take advantage of an approach called transfer learning, in which a trained foundation model is fine-tuned using a smaller data set — in this case, information specific to African populations. Transfer learning has been used successfully for image recognition, with a handful of labelled photos allowing the model to learn new patterns10. We are confident that there are already enough African data available for transfer learning to begin to identify pharmacogenetic variants.
African countries mostly rank low on the AI readiness index published by the UK consultancy Oxford Insights, with sub-Saharan Africa the worst-scoring world region when it comes to how ready governments are to implement AI in public services11. The following changes are needed to ensure that the scientific community in Africa is ready to harness transfer learning — and future AI tools for pharmacogenetic research (see also ‘The future of AI in pharmacogenetics’).
The future of AI in pharmacogenetics
Cutting-edge approaches in artificial intelligence (AI) could one day help to tailor drugs for Africa.
Approach 1. Of all drug classes, anticancer drugs have the most pharmacogenetic information available. This is because anticancer drugs are regularly tested in vitro using cancer cells and ‘mini tumours’ grown in culture dishes, with genetic information collected alongside myriad other biological data. AI models that harness this wealth of data are being used to predict how patients will respond to anticancer drugs. A similar approach that uses ‘mini livers’ and other in vitro models of how drugs are metabolized could be useful for pharmacogenetics beyond anti-cancer drugs.
Approach 2. AI models are becoming adept at predicting when ‘missense’ genetic variants (which modify one amino acid in a protein) will alter a protein’s structure12, and whether that change will prevent the protein from functioning normally. Use of these models to analyse variants that are prevalent in Africa could be followed by mechanistic simulations of how they might affect the drugs that are most often prescribed there. This could help researchers to identify variants of potential pharmacogenetic interest and so prioritize them for further research.
Train African researchers. Scientists in Africa are best positioned both to leverage knowledge of traditional medicine in Africa and to understand disease epidemiology in their regions. Africans, therefore, can best determine the research and data needed for effective drug discovery and drug tailoring on the continent.
International funders, research institutions and pharmaceutical companies must invest in training African researchers in AI and pharmacogenetics. Partnering with African initiatives such as Pharmacometrics Africa — a non-profit organization that provides training in clinical pharmacology — can help institutes to build local capacity.
Keep collecting data. Although transfer learning will be helpful for identifying pharmacogenetic variants that are common across Africa, many more genome sequences and focused clinical-trial results are needed to fully capture pharmacogenetic differences between African populations. Africa is not homogeneous — different ethnogeographical groups should be considered separately, in much the same way as biomedical research considers different European populations. The number of clinical trials on the continent is rising; future trials must include diverse cohorts of people, spanning multiple ethnicities.
Researchers wishing to conduct clinical trials across Africa currently need to apply to the health authorities of each country, each of which has different legal standards, fees and response timelines. When the African Medicines Agency becomes fully operational, clinical-trial regulation should become harmonized across the continent. With the date of this unknown, until then researchers can get help from the Clinical Trials Community — a platform hosted by the clinical software company Nuvoteq in Pretoria, South Africa, that provides up-to-date resources on the various regulatory and ethics requirements for conducting clinical trials in each country in Africa.
Invest in infrastructure and equipment. Genomics facilities are scarce in many African countries. A group of internationally renowned genomics researchers hopes to establish eight centres of excellence in genomics in Africa, each coordinated with a local academic unit and public-health facility. This initiative could bring world-class genomics research to Africa, generating the data needed to identify pharmacogenetic variants. To make this a reality, strong coordination between international funders, local governments and the Africa Centres for Disease Control and Prevention in Addis Ababa, Ethiopia, is needed to ensure that funding for the project materializes.
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And genomics tools such as pharmacogenetics testing kits — which analyse biological samples for thousands of mutations in genes involved in drug responses — must be tailored to Africans. This technology could help clinicians to adapt drug prescriptions to each patient’s genetic characteristics, bringing personalized medicine to Africa. Companies that make these tools, such as the US biotechnology firm Illumina, should work with African scientists to drive the inclusion of Africa-relevant variants as they are discovered.
Develop frameworks for data sharing and ethical research. It is unlikely that a single African institution, city or even country will have all the necessary components — human capacity, research infrastructure and data-collection sites — to be able to conduct world-class pharmacogenetics research. Data sharing is therefore needed.
But local researchers are concerned that openly sharing their data will put them at a disadvantage compared with colleagues in the global north, who might have more resources, infrastructure and skilled personnel to analyse the data and publish their findings. This reluctance is exacerbated by the fact that obtaining ethical approval to share and reuse samples is time-consuming — explaining and obtaining informed consent for genomics research often involves translating consent forms into local languages, for instance.
African scientists must build trusted research networks for sharing of data. These efforts will benefit from having continent-level legal and ethics frameworks for data sharing, enabling cross-country collaborations.
A good example of how data sharing across the continent can stimulate world-class research comes from the Pan-African Bioinformatics Network (H3ABioNet). This research consortium manages data storage and network infrastructure for the Human Heredity and Health in Africa (H3Africa) initiative — the largest effort to coordinate genomics research in Africa so far. The network has facilities in several African countries and well-defined data-submission and access policies for human genomics. It combines locally led management committees with training, good computational infrastructure and clear policies about data quality, allowing results to be deposited in a shared databank and incorporated into international databases such as the European Genome–phenome Archive.
When sharing of open data is restricted — in the case of a patient’s biomedical data, for instance — ‘decentralized’ AI algorithms can help. These are trained on data from numerous institutes, but in a way that allows each institute to keep its own data private. Such cooperative algorithms have been piloted in reference centres and hospitals in Europe and the United States for medical-imaging data.
The first steps towards realizing pharmacogenetics research in Africa are being taken, and AI can play a pivotal part in moving the field forwards. With successful capacity building, people in Africa will benefit from safer and more effective treatments, reducing the cost of health care on the continent. Without it, the disease-eradication goals outlined by the World Health Organization for the current decade are unlikely to be met.
