Differentiation of childhood risk profiles for schizophrenia according to the development of verbal and performance intelligence.

Distinct patterns in the premorbid development of verbal and performance intelligence might differentiate the propensity for later illness among individuals putatively at risk for schizophrenia. This study applied linear mixed-effects models to four verbal and performance intelligence assessments gathered from a community sample of youth (n = 114) over ages 9-20 years, comparing developmental trajectories for at-risk youth with a triad of well-replicated antecedents of schizophrenia (ASz) and at-risk youth with a family history of schizophrenia (FHx), each relative to low-risk, typically developing peers (TD). Supplementary analyses explored heterogeneity among ASz youth according to persistence of symptoms, and FHx youth according to familial loading and presence of antecedents.

Trajectories of change in mothers' parenting confidence and relationship with baby: a 15-month qualitative longitudinal study.

Background: Parents' experiences and their relationship to their baby undergo various changes over the course of the first year. This is particularly the case for mothers, who still tend to take on the primary caregiver role in most families in the UK. Better understanding the changes mothers experience in the first year is of importance given the impact of the parent-infant relationship for children's socio-emotional development.

Is ChatGPT Ready for Public Use in Organ-Specific Drug Toxicity Research?

The growing impact of large language models (LLMs), such as ChatGPT, prompts questions about the reliability of their application in public health. We compared drug toxicity assessments by GPT-4 for liver, heart, and kidney against expert assessments using US Food and Drug Administration (FDA) drug-labeling documents. Two approaches were assessed: a 'General prompt', mimicking the conversational style used by the general public, and an 'Expert prompt' engineered to represent an approach of an expert.

Drug-induced kidney injury: challenges and opportunities.

Drug-induced kidney injury (DIKI) is a frequently reported adverse event, associated with acute kidney injury, chronic kidney disease, and end-stage renal failure. Prospective cohort studies on acute injuries suggest a frequency of around 14%-26% in adult populations and a significant concern in pediatrics with a frequency of 16% being attributed to a drug. In drug discovery and development, renal injury accounts for 8 and 9% of preclinical and clinical failures, respectively, impacting multiple therapeutic areas.

New approach methodologies (NAMs): identifying and overcoming hurdles to accelerated adoption.

New approach methodologies (NAMs) can deliver improved chemical safety assessment through the provision of more protective and/or relevant models that have a reduced reliance on animals. Despite the widely acknowledged benefits offered by NAMs, there continue to be barriers that prevent or limit their application for decision-making in chemical safety assessment. These include barriers related to real and perceived scientific, technical, legislative and economic issues, as well as cultural and societal obstacles that may relate to inertia, familiarity, and comfort with established methods, and around regulatory expectations and acceptance.

Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity.

Drug-induced renal injury (DIRI) causes >1.5 million adverse events annually in the USA alone. Although standard biomarkers exist for DIRI, they lack the sensitivity or specificity to detect nephrotoxicity before the significant loss of renal function.

Academic drug discovery: Challenges and opportunities.

There are many different approaches to drug discovery in academia, some of which are based broadly on the industrial model of discovering novel targets and then conducting screening within academic drug discovery centres to identify hit molecules. Here we describe our approach to drug discovery, which makes more efficient use of the capabilities and resources of the different stakeholders. Specifically, we have created a large portfolio of drug projects and conducted small amounts of derisking work to ensure projects are investment ready.

Reduced prosocial motivation and effort in adolescents with conduct problems and callous-unemotional traits.

Background: Prosocial behaviours - acts that benefit others - are of crucial importance for many species including humans. However, adolescents with conduct problems (CP), unlike their typically developing (TD) peers, demonstrate markedly reduced engagement in prosocial behaviours. This pattern is particularly pronounced in adolescents with CP and high levels of callous-unemotional traits (CP/HCU) who are at increased risk of developing psychopathy in adulthood.

Data science in drug discovery safety: Challenges and opportunities.

Early de-risking of drug targets and chemistry is essential to provide drug projects with the best chance of success. Target safety assessments (TSAs) use target biology, gene and protein expression data, genetic information from humans and animals, and competitor compound intelligence to understand the potential safety risks associated with modulating a drug target. However, there is a vast amount of information, updated daily that must be considered for each TSA.

A generative adversarial network model alternative to animal studies for clinical pathology assessment.

Animal studies are unavoidable in evaluating chemical and drug safety. Generative Adversarial Networks (GANs) can generate synthetic animal data by learning from the legacy animal study results, thus may serve as an alternative approach to assess untested chemicals. AnimalGAN, a GAN method to simulate 38 rat clinical pathology measures, was developed with significant robustness even for the drugs that vary significantly from these used during training, both in terms of chemical structure, drug class, and the year of FDA approval.

'It wasn't the strategies on their own': Exploring caregivers' experiences of accessing services in the development of interventions for autistic people with intellectual disability.

Many autistic individuals with intellectual disability experience anxiety, and for those who use few or no words, anxiety may present as behaviour that challenges, such as self-injury and avoiding anxiety-provoking situations. Families report difficulty accessing support from services for autistic individuals experiencing anxiety. Moreover, once receiving support, effective interventions for autistic people with intellectual disability are limited.

DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application.

The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the International Council for Harmonization (ICH) guidelines. Building on this in silico approach, here we describe DeepAmes, a high performance and robust model developed with a novel deep learning (DL) approach for potential utility in regulatory science.

An integrated approach for early in vitro seizure prediction utilizing hiPSC neurons and human ion channel assays.

Seizure liability remains a significant cause of attrition throughout drug development. Advances in stem cell biology coupled with an increased understanding of the role of ion channels in seizure offer an opportunity for a new paradigm in screening. We assessed the activity of 15 pro-seizurogenic compounds (7 CNS active therapies, 4 GABA receptor antagonists, and 4 other reported seizurogenic compounds) using automated electrophysiology against a panel of 14 ion channels (Nav1.

TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles between Organs, Ages, and Sexes.

Animal studies are required for the evaluation of candidate drugs to ensure patient and volunteer safety. Toxicogenomics is often applied in these studies to gain understanding of the underlying mechanisms of toxicity, which is usually focused on critical organs such as the liver or kidney in young male rats. There is a strong ethical reason to reduce, refine and replace animal use (the 3Rs), where the mapping of data between organs, sexes and ages could reduce the cost and time of drug development.

Science-led regulatory strategies in nonclinical development of new medicines.

The development of a pharmaceutical is a stepwise process involving an evaluation of both animal and human efficacy and safety information. Regulations around drug development exist to protect people and the environment from harm and should create a level playing field for business, allowing well-run companies to thrive. However, adherence to good science should guide decisions rather than rigorously following guidelines, and there is almost always more than one way to get to the ultimate goal.

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net).

US regulations to curb alleged cancer causes are ineffectual and compromised by scientific, constitutional and ethical violations.

The 1958 Delaney amendment to the Federal Food Drug and Cosmetics Act prohibited food additives causing cancer in animals by appropriate tests. Regulators responded by adopting chronic lifetime cancer tests in rodents, soon challenged as inappropriate, for they led to very inconsistent results depending on the subjective choice of animals, test design and conduct, and interpretive assumptions. Presently, decades of discussions and trials have come to conclude it is impossible to translate chronic animal data into verifiable prospects of cancer hazards and risks in humans.

Corrigendum: DeepCarc: Deep learning-powered carcinogenicity prediction using model-level representation.

[This corrects the article DOI: 10.3389/frai.2021.

Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury.

Artificial intelligence (AI) has played a crucial role in advancing biomedical sciences but has yet to have the impact it merits in regulatory science. As the field advances, and approaches have been evaluated as alternatives to animal studies, in a drive to identify and mitigate safety concerns earlier in the drug development process. Although many AI tools are available, their acceptance in regulatory decision-making for drug efficacy and safety evaluation is still a challenge.

Parenting boys with conduct problems and callous-unemotional traits: parent and child perspectives.

Parenting children with conduct problems (CP) is challenging, yet very little research has examined parenting using both quantitative and qualitative methods, from the perspective of the child and their parent/caregiver, and separately for those with high vs. low levels of callous-unemotional traits (HCU vs. LCU).