The World Health Organization Prequalification Program and Clinical Pharmacology in 2030

Cross-disciplinary mathematical modelling to benefit healthcare - could clinical pharmacology play an enabling role?

Clinical pharmacology is often the nexus in any cross-disciplinary team that is seeking solutions for human healthcare issues. The use and application of real-world data and artificial intelligence to better understand our ecosystem has influenced our view at the world and how we do things. This has resulted in remarkable advancements in the healthcare space and development of personalized medicines with great attributes from the application of models and simulations, contributing to a more efficient healthcare development process. A cross-disciplinary symposium was held in December 2023, whereby experts from different scientific disciplines engaged in a high-level discussion on the opportunities and challenges of mathematical models in different fields, possible future developments and decision making. A strong interlink amongst the disciplines represented was apparent, with clinical pharmacology identified as the one which integrates various scientific disciplines. Deliberate and strategic cross-disciplinary dialogues are required to break out of the silos and implement integration for efficiency and cost-effectiveness of novel interventions.

The evolution of HIV self-testing and the introduction of digital interventions to improve HIV self-testing

HIV self-testing (HIVST) complements traditional HIV testing programmes by removing barriers and increasing access to testing for key populations, and digital interventions have been developed for HIVST to improve the testing and linkage to care experience for users. The first HIVST kit was proposed in 1986, but it took 10 years for the home sample collection (HSC) HIVST to become available and another 16 years for rapid diagnostic test HIVST to be approved by the Federal Drug Administration. Since then, studies have shown high usability and performance of HIVST, which led the World Health Organization formally recommending HIVST in 2016, and currently almost 100 countries have incorporated HIVST into their national testing strategy. Despite the popularity, HIVST present challenges around pre-and post-test counselling, as well as the ability to report results and link users to care, and digital interventions for HIVST have been introduced to address these challenges. The first digital intervention for HIVST was introduced in 2014 and showed that digital interventions could be used to distribute HIVST kits, report results and link users to care. Since then, dozens of studies have been conducted, which have validated and expanded on these early findings, but many were pilot studies with small sample sizes and lacked the standardization of indicators required to aggregate data across platforms to prove impact at scale. For digital interventions for HIVST to be championed for scale-up, they must continue to show measurable impact at larger scales, while still maintaining and standardizing data security and integrity.

Challenges to the delivery of clinical diabetes services in Ghana created by the COVID-19 pandemic

OBJECTIVE

The barriers to delivering clinical non-communicable disease services in low- and middle-income countries have risen with the onset of COVID-19. Using Ghana as a case study, this article examines the changes COVID-19 has brought to diabetes service delivery and considers policy responses to deal with future such outbreaks.

METHODS

We conducted 18 interviews between November 2020 and February 2021 with health professionals and administrators from primary, secondary and tertiary facilities within the Ghana Health Service. The analysis was performed using deductive and inductive methods.

RESULTS

There were six general themes in interviewees' responses: (1) COVID-19 had exacerbated the problems of high medicine and service costs and medicine shortages, (2) the pandemic had exacerbated problems of poor patient record keeping, (3) COVID-19 had reduced the availability of suitably trained health providers, (4) staff had become demoralized by management's unwillingness to make innovative changes to cope with the pandemic, (5) COVID-19 led to a reorganization of diabetes services, and (6) the country's national health insurance scheme lacked flexibility in dealing with the pandemic.

CONCLUSIONS

Access to resources is limited in LMICs. However, our study highlights practical policy responses that can improve health providers' response to COVID-19 and future pandemics.

Bendiocarb and Malathion Resistance in Two Major Malaria Vector Populations in Cameroon Is Associated with High Frequency of the G119S Mutation (Ace-1) and Overexpression of Detoxification Genes

The spread of pyrethroid resistance in malaria vectors is a major threat affecting the performance of current control measures. However, there is still not enough information on the resistance profile of mosquitoes to carbamates and organophosphates which could be used as alternatives. The present study assessed the resistance profile of Anopheles gambiae s.l. to bendiocarb and malathion, at the phenotypic and molecular levels, in different eco-epidemiological settings in Cameroon. Anopheles gambiae s.l. mosquitoes were collected from four eco-epidemiological settings across the country and their susceptibility level to bendiocarb and malathion was determined using WHO tubes bioassays. The ace-1 target site G119S mutation was screened by PCR. Reverse Transcription quantitative PCR 3-plex TaqMan assays were used to quantify the level of expression of eight genes associated with metabolic resistance. Resistance to malathion and/or bendiocarb was recorded in all study sites except in mosquitoes collected in Kaélé and Njombé. The Ace-1 (G119S) mutation was detected in high frequencies (>40%) in Kékem and Santchou. Both An. gambiae and An. coluzzii were detected carrying this mutation. The cytochrome P450s gene Cyp6p3 associated with carbamate resistance and the glutathione S-transferase gene Gste2 associated with organophosphate resistance were found to be overexpressed. Genes associated with pyrethroid (Cyp6m2, Cyp9k1, Cyp6p3) and organochlorine (Gste2, Cyp6z1, Cyp6m2) and cuticle resistance (Cyp4g16) were also overexpressed. The rapid spread of resistance to organophosphates and carbamates could seriously compromise future control strategies based on IRS. It is therefore becoming important to assess the magnitude of bendiocarb and malathion resistance countrywide.

Comparative study of the effect of solvents on the efficacy of neonicotinoid insecticides against malaria vector populations across Africa

Background

New insecticides with a novel mode of action such as neonicotinoids have recently been recommended for public health by WHO. Resistance monitoring of such novel insecticides requires a robust protocol to monitor the development of resistance in natural populations. In this study, we comparatively used three different solvents to assess the susceptibility of malaria vectors to neonicotinoids across Africa.

Methods

Mosquitoes were collected from May to July 2021 from three agricultural settings in Cameroon (Njombe-Penja, Nkolondom, and Mangoum), the Democratic Republic of Congo (Ndjili-Brasserie), Ghana (Obuasi), and Uganda (Mayuge). Using the CDC bottle test, we compared the effect of three different solvents (ethanol, acetone, MERO) on the efficacy of neonicotinoids against Anopheles gambiae s.l. In addition, TaqMan assays were used to genotype key pyrethroid-resistant markers in An. gambiae and odds ratio based on Fisher exact test were used to evaluate potential cross-resistance between pyrethroids and clothianidin.

Results

Lower mortality was observed when using absolute ethanol or acetone alone as solvent for clothianidin (11.4‒51.9% mortality in Nkolondom, 31.7‒48.2% in Mangoum, 34.6‒56.1% in Mayuge, 39.4‒45.6% in Obuasi, 83.7‒89.3% in Congo and 71.1‒95.9% in Njombe pendja) compared to acetone + MERO for which 100% mortality were observed for all the populations. Similar observations were done for imidacloprid and acetamiprid. Synergist assays (PBO, DEM and DEF) with clothianidin revealed a significant increase of mortality suggesting that metabolic resistance mechanisms are contributing to the reduced susceptibility. A negative association was observed between the L1014F-kdr mutation and clothianidin resistance with a greater frequency of homozygote resistant mosquitoes among the dead than among survivors (OR = 0.5; P = 0.02). However, the I114T-GSTe2 was in contrast significantly associated with a greater ability to survive clothianidin with a higher frequency of homozygote resistant among survivors than other genotypes (OR = 2.10; P = 0.013).

Conclusions

This study revealed a contrasted susceptibility pattern depending on the solvents with ethanol/acetone resulting to lower mortality, thus possibly overestimating resistance, whereas the MERO consistently showed a greater efficacy of neonicotinoids but it could prevent to detect early resistance development. Therefore, we recommend monitoring the susceptibility using both acetone alone and acetone + MERO (4 µg/ml for clothianidin) to capture the accurate resistance profile of the mosquito populations.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00962-4.

Promoting Sustainability through Next-Generation Biologics Drug Development

The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using “digital twins” can maximize efficiency while improving sustainability. As part of this review, we investigate how the World Health Organization’s 17 sustainability goals can apply toward next-generation drug development. We analyze the state-of-the-art laboratory leadership, inclusive personnel recruiting, the latest therapy approaches, and intelligent process automation. We also outline how modern data science techniques and machine tools for CMC help to shorten drug development time, reduce failure rates, and minimize resource usage. Finally, we systematically analyze and compare existing approaches to our experiences with the high-throughput laboratory KIWI-biolab at the TU Berlin. We describe a sustainable business model that accelerates scientific innovations and supports global action toward a sustainable future.

Scientific considerations for global drug development

Requiring regional or in-country confirmatory clinical trials before approval of drugs already approved elsewhere delays access to medicines in low- and middle-income countries and raises drug costs. Here, we discuss the scientific and technological advances that may reduce the need for in-country or in-region clinical trials for drugs approved in other countries and limitations of these advances that could necessitate in-region clinical studies.

Research of Epidemic Big Data Based on Improved Deep Convolutional Neural Network

In recent years, with the acceleration of the aging process and the aggravation of life pressure, the proportion of chronic epidemics has gradually increased. A large amount of medical data will be generated during the hospitalization of diabetics. It will have important practical significance and social value to discover potential medical laws and valuable information among medical data. In view of this, an improved deep convolutional neural network (“CNN+” for short) algorithm was proposed to predict the changes of diabetes. Firstly, the bagging integrated classification algorithm was used instead of the output layer function of the deep CNN, which can help the improved deep CNN algorithm constructed for the data set of diabetic patients and improve the accuracy of classification. In this way, the “CNN+” algorithm can take the advantages of both the deep CNN and the bagging algorithm. On the one hand, it can extract the potential features of the data set by using the powerful feature extraction ability of deep CNN. On the other hand, the bagging integrated classification algorithm can be used for feature classification, so as to improve the classification accuracy and obtain better disease prediction effect to assist doctors in diagnosis and treatment. Experimental results show that compared with the traditional convolutional neural network and other classification algorithm, the “CNN+” model can get more reliable prediction results.