Year 1 Medic PRE SSC Introduction

2024-02-13

Learning Objectives:

  • Learn about schizophrenia, its aetiology, its treatment, and related challenges.
  • Learn about pharmacogenomic variation and the role this plays in affecting the metabolism of medication and how this might affect treatment outcomes.
  • Understanding medical research, why it is important, and the value of statistical analysis in helping shape therapeutic guidelines and clinician behaviour.

Part 1: Schizophrenia

Overview

  • Age of onset is often during adolescence, and development in childhood or late-adulthood is rare. However, these estimates can vary based on sex, population, and other demographic variables.
    • For example, generally, schizophrenia is slightly more common in men and tends to emerge earlier than in women. Furthermore, these sex differences can pervade not just disorder onset, but also prognosis and treatment response (Abel, Drake, and Goldstein 2010).


Impact

  • One of the leading causes of disability worldwide. Ranked 12th, behind only depression and anxiety in terms of psychiatric disorders and accounting for around 15,000 years lived with disability (Disease Study 2013 Collaborators 2015).

  • The economic burden estimated to be between 0.02% to 1.65% of GDP (Chong et al. 2016).

  • Across seven European countries, people with schizophrenia found to experience on average 25.3 days in hospital per year, alongside high levels of unemployment (Szkultecka-Dębek et al. 2016).

  • Patients tend to report a lack of company and close relationships, disempowerment, and stigmatisation, alongside disruption or distress due to the experience of psychiatric symptoms (Harrison and Gill 2010; Millier et al. 2014).

  • Close relations also report concerns both about the patient but also their future and finances (Thornicroft et al. 2004).



Signs and Symptoms

The Ebers Papyrus, which was an ancient Egyptian medical text, contains the first known description of a schizophrenia-like disorder (1550 BC). Then, over 3000 years later Emil Kraepelin fully described ‘dementia praecox’ in 1899 in his textbook. A bit later still, Eugen Bleuler used the term schizophrenia for the first time (1908).


Symptoms of schizophrenia are broadly categorised as being positive, negative, or cognitive.

Positive Domain

  • Positive symptoms occur in addition to normal behaviour and are often viewed as hallmark symptoms of the disorder (Kahn et al. 2015).
    • Hallucinations are perceptions that aren’t real. They can occur across multiple sensory domains including visual, auditory, olfactory, gustatory, and tactile.
    • Delusions can be described as false beliefs that are at odds with reality. These may relate to a fear of persecution resulting in paranoia, or to thoughts about oneself, often resulting in beliefs of grandeur. They can sometimes arise in response to an experienced hallucination.
    • Disorganised thoughts and speech include when an individual might have difficulty concentrating, report jumbled or hazy thoughts, and jumping between conversation topics without reason.
      Positive symptoms are not unique to schizophrenia, occurring in other forms of psychotic disorders such as schizoaffective disorder, and schizophreniform. Psychotic symptoms are also not uniquely pathological with a surprising proportion of the non-clinical population having some experience of them (Verdoux and Os 2002).

Negative Domain

  • Negative symptoms are viewed as a reduction in typical behaviour and include a range of symptoms that overlap with other psychiatric disorders such as depression.
    • Such symptoms include a reduction in energy and motivation, anhedonia, and social withdrawal, alongside flatness of affect.
  • In the same way that thoughts can become difficult to follow, behaviour and movement can also become disorganised or catatonic.
    • This may look like repetitive motor behaviours, hyperactivity, a marked reduction in voluntary movement or the keeping of unnatural or uncomfortable postures.
  • Cognitive symptoms represent a range of executive dysfunctions which also overlap with other disorders.
  • Signs might include problems with working memory, attention shifting, planning and cognitive flexibility.


Negative and cognitive symptoms of schizophrenia typically present prior to the manifestation of positive symptoms and thus represent a prodromal stage (Kahn et al. 2015). However, due to the overlap of these symptoms with other psychiatric conditions, schizophrenia onset is not often identified until the first episode of psychosis and thus individuals may often be misdiagnosed in the early stages of the disorder.



Diagnosis

  • An individual must experience at least two of the following symptoms for a minimum of 6 months: delusions, hallucination, disorganised speech, disorganised or catatonic behaviour, and negative symptoms.
    • Symptoms must impact functioning in occupational and interpersonal domains.
    • Finally, alternative explanations for symptoms must be ruled out, for example, alternative medical conditions, drug use, and other psychiatric or developmental disorders.
  • Due to the nature of the diagnosis and the range of symptoms that can be experienced, schizophrenia is a highly heterogeneous disorder which can make studying and treating it a challenge.
  • Issues with diagnosis: validity of measurement tool, reliance on self-report etc.



Aetiology

  • Schizophrenia is thought to be caused by a combination of genetic and environmental factors that add up to increase your risk of developing the disorder.
  • This is known as a diathesis-stress model, where genetic variables act as a diathesis, or predisposition to a certain disorder, and the environmental factors are the stress component, which when combined with genetic vulnerability can pass a theoretical threshold for a disorder and trigger its onset.
  • The jar model is another way of thinking about this and has been used to illustrate the concept of risk to mental illness to patients and families.


Figure from [@austin_evidence-based_2020]

Figure from (Austin 2020)



  • Early evidence for the role of genetics in schizophrenia came from twin studies.
  • More recently, GWAS have identified 287 genetic loci that may contain common variants (i.e., small nucleotide polymorphisms, SNPs) conferring risk for schizophrenia (Trubetskoy et al. 2022).
  • However, these risk variants are not fully penetrant. This means that individuals might carry a high number of associated variants but still not develop a given psychiatric disorder.


  • Dopamine is a neurotransmitter involved in reward systems, memory, movement, and motivation. It is made in the substantia nigra, and the ventral tegmental area, and projects out of these areas via the mesolimbic pathway and the mesocortical pathway. The dopamine hypothesis was one of the first widely accepted theories of schizophrenia development and is supported by the use of antipsychotics (usually dopamine antagonists).
    • Mesolimbic dopamine hyperfunction –> positive symptoms.
    • Mesocortical dopamine hypofunction –> negative symptoms.
    • Revised dopamine hypothesis considers a role for other neurotransmitters such as serotonin, glutamate, and GABA.


Figure from [@henley_foundations_2021]

Figure from (Henley 2021)


  • Alternative perspectives for schizophrenia pathogenesis, such as considering the role of the immune system and inflammation, abnormal neural structures, drug use, family environment, urban environment, parental age, migration status, and social adversity.

Management

Schizophrenia is mainly treated with pharmacotherapy in the form of antipsychotic drugs. This is sometimes used in combination with psychological therapies such as Cognitive Behavioural Therapy (CBT) or family therapies. Antipsychotic drugs are dopamine receptor antagonists, but there are differences in receptor target across within and between the two antipsychotic classes.

First Line Treatment

  • First Generation Antipsychotics (FGA), also called typical or convenetional antipsychotics, were developed first, in the around the 1950s. Examples include: Chlorpromazine (Thorazine), Fluphenazine (Prolixin), Haloperidol (Haldol), Loxapine (Loxitane), Pimozide (Orap), and Trifluoperazine (Stelazine).

    • General target: D2 receptors mainly, but also D3 and D4 to a lesser extent.
    • Side Effects: Extra-Pyramidal Side Effects (EPSE - movement problems such as Tardive Dyskinesia or Parkinsonism), metabolic and cardiac Adverse Drug Reactions (ADRs).


  • Second Generation Antipsychotics (SGA) are also known as atypical antipsychotics, and these were developed later in the 1980s. Examples include: Amisulpride (Solian), Aripiprazole (Abilify, Abilify Maintena), Rrisperidone (Risperdal), Olanzapine (Zypadhera, Zyprexa), and Quetiapine (Seroquel).

    • General target: D2 and 5-HT receptors, also Noradrenaline and Glutamate systems.
    • Side Effects: reduced risk for EPSE, but greater risk for metabolic ADRs.


  • Antipsychotics make be taken orally or by injection.
    • Oral: most common route of administration. Easier to take but must be taken more frequently. It is also easier to be non-compliant with oral medication.
    • Depot (Long Acting Injectable/LAI): often offered to people with severe symptoms or who are non-compliant with oral medication.
      • The drug is released slowly so the therapeutic effect lasts longer.
      • Must be administered by a clinician so harder to be non-compliant.


Challenges

There is a lot of variation in drug response, with some people responding better to FGA and others to SGA. Therefore, both classes of drug are commonly prescribed to people with schizophrenia.

  • The biggest challenge is finding a drug that is both effective and tolerable for the individual:
    • The severity of side effects must be balanced by symptom improvement.
    • If patients are prescribed drugs that are ineffective or produce side-effect profiles that are too much for a patient, they might become non-adherent.
    • As efficacy and side-effects for a given drug will vary across individuals, there is a little bit of trial-and-error involved in finding the right drug.
      • Getting people on the antipsychotic best-suited to them fast is very important as it means patients can experience improvement faster.
      • Rapid delivery of appropriate and effective therapy can improve a patient’s prognosis (Lähteenvuo and Tiihonen 2021).

Treatment resistant is a common problem in clinical settings and can affect 20 – 60 % of individuals experiencing psychiatric disorders (Howes, Thase, and Pillinger 2022).

  • In schizophrenia, approximately 30% of individuals do not respond to antipsychotic medication – this is termed Treatment Resistant Schizophrenia (TRS).
  • Described as experiencing no benefit after two or more trials of different antipsychotics at a sufficient dose and for a sufficient length of time.


Clozapine

Clozapine (known as Clozaril, Denzapine, Zaponex) is the only therapy for people with TRS and is the most effective antipsychotic for both schizophrenia and TRS.

  • More severe side effects limit its use to people with TRS.
    • Blood dyscrasias: Neutropenia and Agranulocytosis – low levels of neutrophils which results in immune suppression and can be fatal.
      • Agranulocytosis is a more severe version of neutropenia.
      • Occurs in ~1% (agranulocytosis) and ~3% (neutropenia) of patients.
    • In the UK, patients must undergo Therapeutic Blood Monitoring to ensure their neutrophil levels are in the healthy range. If their Absolute Neutrophil Count (ANC) falls into the criteria of neutropenia (<1500) then clozapine medication is stopped.
    • These risks and the requirement for blood monitoring make clinicians reluctant to prescribe clozapine and so it’s very much under-utilised despite its effectiveness.


The table below comes from NHS (SLaM) guidance for Clozapine and Blood Dyscrasias in patients with coronavirus (COVID-19). BEN is Benign Ethnic Neutropenia, which describes lower baseline neutrophil levels that do not result in immune risk. It is thought to arise due to the Duffy-Null genotype, that is often seen in people of African and Middle Eastern ancestries. As people with BEN have lower baseline neutrophils then their criteria are more liberal to account for this.


Blood counts (x10^9/L) Classification Action
WBC >= 3.5
AND
neutrophils >= 2		 Green Continue Clozapine Treatment
WBC >= 3 and < 3.5
AND / OR
neutrophils >= 1.5 and < 2		 Amber Increase Monitoring Frequency
WBC < 2.5
AND / OR
neutrophils < 1		 Red STOP Clozapine Treatment Immediately


Part 2: Pharmacogenomics

Introduction

The aim of drug titration is to find the optimum dose for a given patient.

Optimum dose = maximum therapeutic effect & minimum ADR (adverse drug reactions) risk.

Pharmacogenomics (PGx) explores the impact of genetic variation on aspects of the pharmacological response, with the aim of applying findings to improve patients’ treatment outcomes through individualising medication plans (Lauschke, Milani, and Ingelman-Sundberg 2017).

  • Applications of pharmacogenomics (often abbreviated as PGx) include:
  • Inferring optimum drug dosage for a patient.
  • Inferring best suited drug for a patient.
  • Identifying patients at risk for adverse drug reactions (ADR).
  • Identifying patients who might be treatment non-responsive.
  • PGx is currently already used for:
    • Cancers
    • Cystic fibrosis
    • HIV



The drug-body relationship can be captured by pharmacokinetic (PK) and pharmacodynamic (PD) effects.

  • Pharmacokinetics: impact of the body on the drug in question, or how the drug moves through and is changed by the body (often via ADME processes).
  • Pharmacodynamics: impact of the drug on the body, or how it affects different aspects of our physiology from a cellular level up to a systems level.


How pharmacogenomic variants may impact the drug-body relationship via pharmacokinetic and pharmacodynamic processes. Image adapted from [@pirmohamed_genetic_2001]

How pharmacogenomic variants may impact the drug-body relationship via pharmacokinetic and pharmacodynamic processes. Image adapted from (Pirmohamed and Park 2001)



Metabolism of Antipsychotics

Enzymes recap: https://www.thesciencehive.co.uk/enzymes-alevel
Proteins recap: https://www.thesciencehive.co.uk/dna-and-protein-synthesis-aqa

Pharmacogenes = genes that code for pharmacologically-relevant proteins and have an impact on the pharmacokinetic and pharmacodynamic processes underlying drug action in the body.

The Cytochrome P450 (CYP) protein family are important to the metabolism of many commonly used drugs. The enzymes CYP1A2, CYP2D6, and CYP3A4 are particularly key for antipsychotic metabolism, as shown in Table 1 and Table 2.

  • There are many other CYP enzymes and some of these play a role in the metabolism of other psychiatric drugs such as CYP2C19 and SSRIs (a type of antidepressant).
  • In humans there are 57 genes across 18 families, however, across different organisms there are over 300,000 different CYP proteins.

Variation or polymorphisms in genes coding CYP enzymes might result in increased or decreased enzymatic activity, or even complete loss of function. This impacts how antipsychotic medication is metabolised in the body and highlights the importance of considering an individual’s relevant pharmacogenomic variants when prescribing antipsychotic medication.


Antipsychotic CYP1A2 CYP2D6 CYP3A4
Chlorpromazine 🗸 🗸 ✗
Fluphenazine ✗ 🗸 ✗
Haloperidol ✗ 🗸 🗸
Loxapine 🗸 🗸 🗸
Perphenazine 🗸 🗸 ✗
Pimozide 🗸 🗸 🗸
Thioridazine 🗸 🗸 ✗
Thiothixene 🗸 ✗ ✗
Trifluroperazine 🗸 ✗ ✗

Table 1. First-generation antipsychotic medication metabolised by CYP enzymes (Pouget et al. 2014; Wijesinghe 2016)



Antipsychotic CYP1A2 CYP2D6 CYP3A4
Aripirazole ✗ 🗸 🗸
Clozapine 🗸 🗸 🗸
Iloperidone ✗ 🗸 🗸
Lurasidone ✗ ✗ 🗸
Olanzapine 🗸 🗸 ✗
Quetiapine ✗ ✗ 🗸
Risperidone ✗ 🗸 🗸
Ziprasidone ✗ ✗ 🗸

Table 2. Second-generation antipsychotic medication metabolised by CYP enzymes (Pouget et al. 2014; Wijesinghe 2016).



Metabolism and PGx Variation

Drug metabolism may be measured with clearance methods.

  • For a given allele, metabolic capacity can be determined through the administration of a probe drug and measuring the concentration of the probe drug and resultant metabolites in bodily fluids, such as blood, urine, and saliva (Gaedigk 2013).
  • This is a non-invasive measure allowing the calculation of a metabolic ratio which helps to inform the impact of the genotype on metabolism phenotype, however alternative methods of measuring drug clearance can be used.

Important - we cannot know metabolism status from PGx variants, but we can infer it based on past literature. The validity of our inferences is determined in part by the quality of past research.

Understanding Genetic Data

We determine whether people have PGx alleles from their genetic information which can be obtained through genotyping or via sequencing.

  • Genotyping: less coverage as we are looking at what variants exist at various locations. The missing gaps in the DNA sequence can be filled in with our knowledge of linkage disequilibrium and imputation.
  • Sequencing: more coverage as we are looking at the order of all nucleotides in the exome (all coding regions) or the genome (the entire DNA sequence). This is different from genotyping which looks at what nucleotides exist in certain, pre-defined places. This is more expensive but provides slightly better results, especially in the case of highly polymorphic pharmacogenes with lots of copy number variation, such as CYP2D6.


Genotyping vs Sequencing. Image from https://www.aboutgeneticcounselors.com/

Genotyping vs Sequencing. Image from https://www.aboutgeneticcounselors.com/


  • We can look to see whether they have any alleles in pharmacogenes for the drug that we are interested in. If they do have them, we can look to see what the effect of those PGx variants are on drug metabolism.
    • A common way of doing this is by categorising people based on their metabolism status across enzymes.


Metabolism Phenotypes. Image from X

Metabolism Phenotypes. Image from X



Example Clinical Workflow

  • Patient with schizophrenia prescribed with fluphenazine (CYP2D6-metabolised conventional antipsychotic).
  • Patient undergoes genetic testing (via genotyping or sequencing) and based on this PGx alleles can be called for the relevant pharmacogenes of interest. In this case, CYP2D6 * (star-)alleles as fluphenazine is a CYP2D6 metabolised drug.

An example workflow is shown below



Metabolism Phenotypes. Image from X

Metabolism Phenotypes. Image from X


Potential Results may include the patient being a slow CYP2D6 metaboliser (i.e., Poor or Intermediate Metaboliser), a normal CYP2D6 metaboliser, or a fast CYP2D6 metaboliser (Rapid or Ultra-rapid metaboliser).

  • Slow Metabolisers: metabolise fluphenazine slower, therefore active compound remains in body for a longer time period and at higher concentration than normal.
    • At greater risk of ADRs due to higher fluphenazine concentration in blood so lower drug tolerance. Might increase risk of medication non-adherance.
    • Patient might require lower fluphenzine dose for the same therapeutic effect as CYP2D6 normal metabolisers.
  • Normal Metabolisers: proceed as usual with fluphenazine titration.
  • Fast Metabolisers: metabolise fluphenazine quicker, therefore active compound remains in body for shorter period of time and at lower concentration.
    • Might experience limited therapeutic success.
    • Simultaneously, at lower risk of ADRs due to its lower concentration so higher drug tolerance.
    • Patient might require higher drug dose for the same therapeutic effect as CYP2D6 normal metabolisers.



Challenges

  • Challenges with identifying PGx variation
    • PGx star alleles may be made of a single, or multiple SNPs.
      • Identifying the correct star allele can be challenging as if one SNP is incorrectly typed then the wrong star-allele might be called and thus incorrect conclusions may be drawn about patient’s enzymatic status.
    • Quality of genetic data.
      • Genotyping: searching for small genetic changes by examining parts of an individual’s DNA sequence and comparing it to a full reference sequence.
      • Sequencing: determining the sequence of nucleotides of an individual’s exome (all coding regions) or the entire genome.


  • Reliance on past research:
    • Inconsistent terminology and unclear categorisation.
    • Quality of previous studies.
    • International consortia have been developed to improve and advance PGx research through the adoption of clear and accepted terminology, alongside collating the literature to help researchers make decisions about the effect of given PGx star alleles.


  • Multiple enzymes and the role of phenoconversion:
    • Some drugs metabolised by multiple enzymes so need to consider PGx variation across a two or more pharmacogenes (e.g., Clozapine).
    • Other factors (other than PGx variation) might influence enzyme activity including other medication and lifestyle variables.
      • Phenoconversion = difference between inferred enzyme activity and actual enzyme activity.
      • Oral contraception and types of antidepressants can inhibit CYP enzymes (e.g., CYP2D6).
      • Cigarette smoke and heavy caffeine use can induce CYP enzymes (i.e., CYP1A2).
      • Inflammation can also inhibit CYP enzyme function.
  • Recent work:
    • Guiding prescription with results from PGx panel helped to reduce clinically relevant ADRs in a multi-centre study across 7 European countries (Swen et al. 2023).
    • Using PGx information in comprehensive medication management plan helped to reduce costs by approximately $7000 per patient (Jarvis et al. 2022).

Part 3: Medical Research

Importance of Research

  • Understand the causes of disease –> informs preventative medicine.
    • E.g., Type 2 diabetes has a genetic and environmental component. Can implement lifestyle interventions that can help prevent the onset of the disorder.
      • Reduces the number of people developing the disorder, and thus the number who require treatment.
  • Understand the development of disorders, alongside its trajectory.
  • Understand drug efficacy and safety via multi-phase clinical trials.
  • Understand disease/disorder prognosis of various diseases and recovery timelines.
  • Discover new treatments and understand when these are likely to work and for who.
    • E.g., Stroke caused by fatty deposits leading to stenosis of the carotid artery. Plaques may break off and block the artery causing stroke. This stenosis may be treated with a carotid endarterectomy. However, the operation itself may cause stroke.
    • RCTs tested whether it was better to give patients the operation or to continue with non-surgical interventions (Evans et al. 2011).
      • The surgery was providing a benefit to patients dependent on the degree of stenosis patients were experiencing. Benefits of surgery outweighed the costs in severe cases but not in cases where stenosis was mild.
      • This changed how doctors treated people at risk of strokes, with people at lower risk being offered alternative, non-surgical interventions.
  • Our understanding of best practice for a given disease, disorder, or injury comes from research.

Good v Bad Research

  • Good research improves the quality of patient care.
  • Bad research can harm patients through the research process or its implications.
    • Unethical research –> Tuskegee Syphilis study, MKULTRA, Nazi experiments.
    • Poor methodology –> leads to incorrect conclusions.
      • A study (Pirosca et al. 2022) explored risk of bias across 1,640 randomised controlled trials (RCTs). Three-fifths were deemed bad due to a high risk of bias. Over half of the participants were in poorly designed research trials.
        • Varied based on type of study conducted. Included studies on Drugs and alcohol, or colorectal research were deemed at high risk of bias.
        • Anaesthesia and haematological research had the greatest proportion of research (over 50%) with a low risk of bias.
    • Waste of time and money, this also means that the conclusions from these studies may not be valid, which of course does not help to inform our care of patients.

This is a good chapter to read, if interested! https://www.ncbi.nlm.nih.gov/books/NBK66194/?report=reader

Our research

Research skills are important for doctors and other clinicians because:

  • Read and understand scientific literature
  • Evaluate strength of clinician evidence
  • Conducting your own research

In the next session we will be:

  • Learning about the steps involved in doing research.
  • Learning about some statistical techniques (regression and meta analysis).
  • Getting experience with data visualization and data analysis in JASP.
  • Interpreting and understanding our results.
  • Understanding what you’ll be assessed on.

Before the next session, it would be great if you could download JASP using this link: https://jasp-stats.org/download/.

References

This is not a reading list :)

Abel, Kathryn M., Richard Drake, and Jill M. Goldstein. 2010. “Sex Differences in Schizophrenia.” International Review of Psychiatry (Abingdon, England) 22 (5): 417–28. https://doi.org/10.3109/09540261.2010.515205.
Austin, Jehannine C. 2020. “Evidence-Based Genetic Counseling for Psychiatric Disorders: A Road Map.” Cold Spring Harbor Perspectives in Medicine 10 (6): a036608. https://doi.org/10.1101/cshperspect.a036608.
Chong, Huey Yi, Siew Li Teoh, David Bin-Chia Wu, Surachai Kotirum, Chiun-Fang Chiou, and Nathorn Chaiyakunapruk. 2016. “Global Economic Burden of Schizophrenia: A Systematic Review.” Neuropsychiatric Disease and Treatment 12 (February): 357–73. https://doi.org/10.2147/NDT.S96649.
Disease Study 2013 Collaborators, Global Burden of. 2015. “Global, Regional, and National Incidence, Prevalence, and Years Lived with Disability for 301 Acute and Chronic Diseases and Injuries in 188 Countries, 1990–2013: A Systematic Analysis for the Global Burden of Disease Study 2013.” Lancet 386 (9995): 743–800. https://doi.org/10.1016/S0140-6736(15)60692-4.
Evans, Imogen, Hazel Thornton, Iain Chalmers, and Paul Glasziou. 2011. Research – Good, Bad and Unnecessary. Pinter & Martin. https://www.ncbi.nlm.nih.gov/books/NBK66209/.
Gaedigk, Andrea. 2013. “Complexities of CYP2D6 Gene Analysis and Interpretation.” International Review of Psychiatry 25 (5): 534–53. https://doi.org/10.3109/09540261.2013.825581.
Harrison, J., and A. Gill. 2010. “The Experience and Consequences of People with Mental Health Problems, the Impact of Stigma Upon People with Schizophrenia: A Way Forward.” Journal of Psychiatric and Mental Health Nursing 17 (3): 242–50. https://doi.org/10.1111/j.1365-2850.2009.01506.x.
Health and Care Excellence, National Institute for. 2011. “Psychosis and Schizophrenia in Children and Young People: Final Scope.”
Henley, Casey. 2021. Foundations of Neuroscience. Michigan State University Libraries. https://openbooks.lib.msu.edu/neuroscience/.
Howes, Oliver D., Michael E. Thase, and Toby Pillinger. 2022. “Treatment Resistance in Psychiatry: State of the Art and New Directions.” Molecular Psychiatry 27 (1): 58–72. https://doi.org/10.1038/s41380-021-01200-3.
Jarvis, Joseph P., Arul Prakasam Peter, Murray Keogh, Vince Baldasare, Gina M. Beanland, Zachary T. Wilkerson, Steven Kradel, and Jeffrey A. Shaman. 2022. “Real-World Impact of a Pharmacogenomics-Enriched Comprehensive Medication Management Program.” Journal of Personalized Medicine 12 (3): 421. https://doi.org/10.3390/jpm12030421.
Kahn, René S., Iris E. Sommer, Robin M. Murray, Andreas Meyer-Lindenberg, Daniel R. Weinberger, Tyrone D. Cannon, Michael O’Donovan, et al. 2015. “Schizophrenia.” Nature Reviews Disease Primers 1 (1): 15067. https://doi.org/10.1038/nrdp.2015.67.
Kirkbride, James B., Antonia Errazuriz, Tim J. Croudace, Craig Morgan, Daniel Jackson, Jane Boydell, Robin M. Murray, and Peter B. Jones. 2012. “Incidence of Schizophrenia and Other Psychoses in England, 1950–2009: A Systematic Review and Meta-Analyses.” PLOS ONE 7 (3): e31660. https://doi.org/10.1371/journal.pone.0031660.
Lähteenvuo, Markku, and Jari Tiihonen. 2021. “Antipsychotic Polypharmacy for the Management of Schizophrenia: Evidence and Recommendations.” Drugs 81 (11): 1273–84. https://doi.org/10.1007/s40265-021-01556-4.
Lauschke, Volker M., Lili Milani, and Magnus Ingelman-Sundberg. 2017. “Pharmacogenomic Biomarkers for Improved Drug Therapy—Recent Progress and Future Developments.” The AAPS Journal 20 (1): 4. https://doi.org/10.1208/s12248-017-0161-x.
McGrath, John, Sukanta Saha, David Chant, and Joy Welham. 2008. “Schizophrenia: A Concise Overview of Incidence, Prevalence, and Mortality.” Epidemiologic Reviews 30: 67–76. https://doi.org/10.1093/epirev/mxn001.
Millier, A., U. Schmidt, M. C. Angermeyer, D. Chauhan, V. Murthy, M. Toumi, and N. Cadi-Soussi. 2014. “Humanistic Burden in Schizophrenia: A Literature Review.” Journal of Psychiatric Research 54 (July): 85–93. https://doi.org/10.1016/j.jpsychires.2014.03.021.
Pirmohamed, Munir, and B. Kevin Park. 2001. “Genetic Susceptibility to Adverse Drug Reactions.” Trends in Pharmacological Sciences 22 (6): 298–305. https://doi.org/10.1016/S0165-6147(00)01717-X.
Pirosca, Stefania, Frances Shiely, Mike Clarke, and Shaun Treweek. 2022. “Tolerating Bad Health Research: The Continuing Scandal.” Trials 23 (1): 458. https://doi.org/10.1186/s13063-022-06415-5.
Pouget, Jennie G., Tahireh A. Shams, Arun K. Tiwari, and Daniel J. Müller. 2014. “Pharmacogenetics and Outcome with Antipsychotic Drugs.” Dialogues in Clinical Neuroscience 16 (4): 555–66. https://doi.org/10.31887/DCNS.2014.16.4/jpouget.
Swen, Jesse J, Cathelijne H van der Wouden, Lisanne EN Manson, Heshu Abdullah-Koolmees, Kathrin Blagec, Tanja Blagus, Stefan Böhringer, et al. 2023. “A 12-Gene Pharmacogenetic Panel to Prevent Adverse Drug Reactions: An Open-Label, Multicentre, Controlled, Cluster-Randomised Crossover Implementation Study.” The Lancet 401 (10374): 347–56. https://doi.org/10.1016/S0140-6736(22)01841-4.
Szkultecka-Dębek, Monika, Katarzyna Miernik, Jarosław Stelmachowski, Miro Jakovljević, Vlado Jukić, Kaire Aadamsoo, Sven Janno, et al. 2016. “Schizophrenia Causes Significant Burden to Patients’ and Caregivers’ Lives.” Psychiatria Danubina 28 (2): 104–10.
Thornicroft, Graham, Michele Tansella, Thomas Becker, Martin Knapp, Morven Leese, Aart Schene, and José Luis Vazquez-Barquero. 2004. “The Personal Impact of Schizophrenia in Europe.” Schizophrenia Research 69 (2): 125–32. https://doi.org/10.1016/S0920-9964(03)00191-9.
Trubetskoy, Vassily, Antonio F. Pardiñas, Ting Qi, Georgia Panagiotaropoulou, Swapnil Awasthi, Tim B. Bigdeli, Julien Bryois, et al. 2022. “Mapping Genomic Loci Implicates Genes and Synaptic Biology in Schizophrenia.” Nature 604 (7906): 502–8. https://doi.org/10.1038/s41586-022-04434-5.
Verdoux, Hélène, and Jim van Os. 2002. “Psychotic Symptoms in Non-Clinical Populations and the Continuum of Psychosis.” Schizophrenia Research, NATO Advanced Research Workshop on Schizophrenia and Schizotypy, held March 24-27, 2001, at Il Ciocco in Tuscany Italy, in honour of Peter Venables, 54 (1): 59–65. https://doi.org/10.1016/S0920-9964(01)00352-8.
Wijesinghe, Ruki. 2016. “A Review of Pharmacokinetic and Pharmacodynamic Interactions with Antipsychotics.” Mental Health Clinician 6 (1): 21–27. https://doi.org/10.9740/mhc.2016.01.021.