Can we predict adverse reactions?

Can we predict adverse reactions?

One of the big challenges in drug development is our limited ability to identify potential adverse reactions associated with new therapeutics. Pharmacogenomics provides a great opportunity in understanding the mechanisms of action of drugs and predict not just their adverse reactions but also their efficacy. But as all opportunities it has some limitations too. In this review we will discuss the usage of pharmacogenetics in drug development and adverse reactions prediction.

But let’s start with what is adverse reaction and why it is important in drug development. Adverse drug reactions include a range of expected (and unexpected) toxicities to therapeutic failures and rare, severe reactions. Monitoring and preventing these reactions is top priority in drug development. How much we can predict the adverse reactions depends on various factors. In some cases where the nature of the studied drug is known there are some anticipated adverse reactions; similarly if the metabolic pathway of the drug is known there are some expected adverse reactions.

How could pharmacogenomics contribute in monitoring drug safety?

For example, codeine is activated to morphine by liver enzyme CYP2D6, however if the patient has multiple copies of active CYP2D6 gene they may be exposed to higher doses of morphine. If the enzyme is with low activities, on the other side, patients will have lower levels of active drug. This same enzyme is responsible for activating one of the cancer therapeutics – tamoxifen – and patients with low activity of the enzyme could be exposed to lower doses of tamoxifen. So why is this important? Patients with active CYP2D6 could be at risk of overdose when taking codeine; cancer patients who do not metabolise well tamoxifen will have lower doses of active drug and this could affect their treatment.

In some cases the consequences are quite drastic – for example, data from clinical trials with patients with metastatic colorectal cancer show that if the tumor cells active mutation in KRAS gene this leads to lack of effect of the anti-cancer drugs, cetuximab and panitumumab.

All these examples show the importance of genetic information when treating different medical conditions, which is why some clinical trials are collecting biogenetic markers for analysis.

What are the challenges in using pharmacogenetics information?

  • There is still limited data regarding many drugs – sometimes this is result of patent protections, in other cases just lack of data or unknown drug action mechanism or metabolic pathway.
  • In cases of very limited treatment options for the patients there is an ethical dilemma is patients have to be excluded from treatment because of unfavourable genetic profile.
  • Genetic testing is expensive and adds cost to patients’ treatment.
  • Collecting information after drug approval is out of drug developers’ control.

While there are challenges in using pharmacogenomics methods in identifying adverse reactions, it will have its place in the future of drug development.


Pharmacogenomic strategies in drug safety

Published on 1 May 2019

Author: Olga Peycheva, Director at Solutions OP Ltd. 
Olga has been working in clinical research since 2005 and has extensive experience in Eastern and Western Europe

Can AL help speed up drug development?

Can AL help speed up drug development?

Artificial intelligence and machine-learning are novel exciting tools which we hope will help drug development. AI is analysis of very large data sets with statistical machine-learning methods. The simple way of explaining the role of AI is that it could be used in drug development to analyse big sets of data in order to identify potential new drugs.

But before we discuss challenges and opportunities that AI could provide we can explore a bit more in the current drug development process. According to the statistics there are 16 new molecular entities launched per year between 1950 and 2014. Slow drug development process and all its challenges cause costs to go up by 8% per year. Some of the potential challenges could be that little is known about the biological mechanisms of the particular disease; the potential drug target is difficult to be reach or result in other complications; some molecules are from classes, which cannot be used as drugs due to various reasons, etc. Another factor is publication bias and patent limitations which prevent scientists, working in drug development to have adequate information about potential molecules. The tendency of pharma companies to outsource parts of drug development process to CROs is another challenge for scientists because it prevents them from access to important information.

While artificial intelligence and big data are bring exiting new opportunities they also have their own risks.

  • One of the biggest risks of creating big data base with all information in medical chemistry will be that may end up with `everyone doing the same thing`;
  • Identifying new compounds is very complex process;
  • AI has been tested mainly on training data so far;
  • Lack of interpretability – it does not show what parameters it has taken into account to produce the final result;
  • There is no method to assess how adequate are the final results which could potentially result in increased costs;
  • There is a risk of algorithmic bias.

All these risks shows that in order to use artificial intelligence you need a large sets of data that could be used to train and establish method to analyse and interpret the final results. However, this should not discourage the usage of AI in drug development because there are great opportunities ahead of it.


Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence?

Published on 1 April 2019

Author: Olga Peycheva, Director at Solutions OP Ltd. 
Olga has been working in clinical research since 2005 and has extensive experience in Eastern and Western Europe

3D printing and its potential application in drug development

3D printing and its potential application in drug development

Three-dimensional printing (3D printing) is gaining popularity in different areas from manufacturing to medicine. It is believed to be considered the beginning of the new industrial revolution.  Not surprising 3D printing is also gaining popularity in drug development although its potential is not fully discovered. Currently 3D printing is explored as a potential method for producing solid oral dosage forms (like tablets and capsules) and its role in developing personalised medicines. However, like all new technologies 3D printing has its advantages and challenges.

What types of 3D printing technologies are used?

  • Vat photopolymerisation is a process that utilises a light source (e.g., laser) to selectively cure a vat of liquid photopolymer, transforming it into a solid object. Examples of such are stereolithography (SLA), digital light processing (DLP), and continuous liquid interface production (CLIP) technologies;
  • Binder jetting (BJ) revolves around the selective binding of solid powder particles by spraying a liquid agent;
  • Powder bed fusion is a selective thermal process that involves the fusion of powder particles by the application of a laser or other heat source. It includes selective laser sintering (SLS), multijet fusion (MJF), direct metal laser sintering/selective laser melting (DMLS/SLM), and electron beam melting (EBM);
  • Material jetting is a selective technique in which liquid droplets of materials are deposited on a surface. These droplets spontaneously solidify [known as drop-on-demand (DOD)] or can be cured or fused using an ultraviolet (UV) light [known as material jetting (MJ)] or a heat source [known as nanoparticle jetting (NPJ)]; 5
  • Direct energy deposition is a process that selectively deposits a form of focused thermal energy (e.g., laser) directly onto powder particles, causing them to melt and fuse. It involves two technologies; laser engineering net shape (LENS) and electron beam additive manufacturing (EBAM);
  • Sheet lamination; compromises the bonding of materials in the form of sheets (e.g., cut paper, plastic or metal) to fabricate 3D objects. It is often known as laminated object manufacturing (LOM) or ultrasonic additive manufacturing (UAM);
  • Material extrusion is a technology that involves the selective dispensing of material in a semisolid form. This technology is further subdivided into fused deposition modelling (FDM), which utilises thermoplastics, and semisolid extrusion (SSE), which utilises gels and pastes.

Advantages of 3D printing in drug development

  • Drug research is an extensive and expensive process, which requires sophisticated supply chain. 3D printing can reduce the costs of clinical research phase by allowing producing small or ‘one-off’ batches of formulations or drugs. This is especially important in early stage – in drug discovery, pre-clinical studies and first in human (FIH) studies. For example, chemists from University of Glasgow have produced successfully ibuprofen. Another team has synthetized baclofen.
  • This method could be very successful in producing different molecules on a small scale which normally have high cost or poor stability.
  • 3D printing could also enable producing drugs on a small scale in remote locations, which otherwise will not support the process.
  • 3D printing was used in pre-clinical drug discovery by producing 3D print of animal and human tissues, which allows these tissues to be used in studying drug toxicity and metabolism. For example, team from Harvard University was able to 3D print the first cardiac microphysiological device. Also there are number of organs that have been 3D printed like stomach, pancreas and small intestine, which gives new opportunities for in vitro drug testing and reduce the number of animal models.
  • 3D printing could speed up the drug manufacturing process on a small scale.
  • 3D printing does not require serious modifications and major labour input.
  • 3D printing is fast – it could take average of 6.5 min to produce a small object which will take 3.5 up to 11.5 hours with conventional methods.

Challenges in 3D printing

  • The biggest challenge is the high price of the 3D printer, which could vary from £1500 to £4 million.
  • Another big issue is potential toxicity due to presence of unreacted monomers.
  • The final product of the 3D printing has low mechanical properties – low friability and hardness values.
  • Another potential risk is drug degradation during the 3D process because of the high temperatures that are used during printing.

While current 3D printing technologies have their limitations it is still early phase of development and probably in the future some of these challenges will be overcame. 3D printing is definitely existed field which has its potential application in drug development.


Reshaping drug development using 3D printing

Published on 1 March 2019

Author: Olga Peycheva, Director at Solutions OP Ltd. 
Olga has been working in clinical research since 2005 and has extensive experience in Eastern and Western Europe

Why new drugs fail?

Why new drugs fail?

According to current data average time and cost of a new drug to reach the market is 12 years and costs £1.15 billion but what is not included in this statistics is the fact that many new potential drugs never make it to the market due to various reasons.

European Center for Pharmaceutical Medicine has summarised the following reasons for late stage drug testing failure:

  • Scientific – the animal models are not properly related to human disease; poor understanding of the disease or just the drug is ineffective;
  • Clinical Study Design – some of the reasons in failure in phase 3 studies is that patients are selected on a different criteria than phase 2 studies, the studies are not able to confirm the results from phase 2 or the measured outcomes were insensitive.
  • Inappropriate Study Design – it may not show efficacy or the patient population is too small to perform adequate evaluation.
  • Dose Selection – may not be adequate for phase 3 studies if the results from phase 2.
  • Data Collection and Analysis – false positive signals from phase 2 or overoptimistic assumptions.
  • Operational Execution – related to data integrity issues or GCP violations.

Adaptive clinical trials design is one of the novel approaches which aims to reduce drug development timelines and costs.

While there is no simple solution to these issues one of the possible approaches is to invest more time in creating optimal study design and make sure the correct endpoints are selected. While timelines are important setting up the study right from the beginning is critical for its success.


Accelerating clinical development timelines

Published on 5 Feb 2019

Author: Olga Peycheva, Director at Solutions OP Ltd. 
Olga has been working in clinical research since 2005 and has extensive experience in Eastern and Western Europe

Innovative Clinical Trial Designs: overview

Innovative Clinical Trial Designs: overview

Those who have worked on clinical trials for many years probably remember the rigidity of the clinical protocols and how often clinicians complained that the study design is out of touch with reality. The new tendency of using innovative clinical trial designs shows that this message was heard. Clinical research has also changed during the years and now we know a lot more about the complexity of diseases like cancer.

What are the limitations of the standard randomized clinical trials?

In a standard study there will be 2-3 options to choose from, it will randomize 1-5% of the eligible patients who consent and in the next 5 to 10 years it will try to catch up with the recruitment that is falling behind and reduce the amount of protocol violations. Data and Safety Monitoring Committee could potentially request early termination. The data will be analysis according to Intent-to-Treat (ITT) principle – counting all outcomes according to randomization, regardless of changes in adherence. This, of course, will trigger long arguments around the data generated from the study its validity, if the correct group was selected, etc.

What innovative designs could be used in cancer clinical trials?

  1. Single-Arm Dose-Finding Studies

The purpose of these studies which are common in phase 1 clinical trials is to find the balance between determining the maximum toxicity dose (MTD) and safe treatment of patients where the dose will be close to the unknown MTD so they can have better chance to benefit from the treatment. Normally phase 1 oncology clinical trials involve small number of patients (20-30) and a model-based approach could be used to determine the most appropriate dose. In this case when the drug reaches phase 3 the researchers can used all data from phase 1 and phase 2 – response and toxicity data – to design phase 3 study. The standard settings do not use such approach.

  1. Biomarker-Based Personalized Therapies: Development and Testing

This approach involves identifying and using relevant biomarkers (for example, tissue samples from tumor – fixed, fresh or circulating tumor cells). The second step is to identify reliable method to assess these markers and the third set is to design clinical trials that support development and verification of personalized therapies.

  1. Seamless Phase 2-3 Randomized Clinical Trials

Small single-arm phase 2 cancer clinical trials cannot use big resources until there is more reliable data that the treatment has potential to be successful. In such cases it may be useful to include phase 2 study as an internal pilot of the confirmatory phase 3 trial.

  1. Comparative effectiveness research studies: Equipoise-Stratified Randomization

The authors of the paper discuss STAR*D study, which offers different treatment options for patients with depression. The study offered 7 possible treatment options and for those patients who did not achieve satisfactory results there were 4 switch options and 3 augment options which allowed clinicians to select the best option for their patients.

  1. Comparative effectiveness research studies: Sequential Multiple-Assignment Randomization (SMAR)

Using the same study as above for example the authors present another option for adaptive study design but in this case the treatment is adaptive. In order to improve the outcome of the treatment patients were randomized on different treatment options based on their medical history and response to previous treatments.

  1. Comparative effectiveness research studies: Embedded Experiments to Close the Knowledge-Action Gap

This approach supports the idea that patients are randomized on the treatment which is superior to the standard of care. One of the challenges of new treatments is that they are implemented in clinical practice very slow and the purpose of this approach is to provide clinicians with more reassurance that the treatment will offer better outcome for this patients.

There is a clear need for changes in protocol designs to align them with clinical practice and provide more flexibility for patients and clinicians.


Innovative Clinical Trial Designs: Toward a 21st-Century Health Care System

Published on 9 Jan 2019

Author: Olga Peycheva, Director at Solutions OP Ltd. 
Olga has been working in clinical research since 2005 and has extensive experience in Eastern and Western Europe

Review: Is clinical trial design fit for purpose?

Review: Is clinical trial design fit for purpose?

It is not a secret that the research and development in the pharmaceutical industry is very costly, time-consuming, risky and not particularly efficient. According to estimates bringing new drugs on the market could cost between $800 million and $2 billion. Failure in late stages – phase 2 and phase 3 – contribute significantly for the increased expenses. Some of the big challenges of phase 2 studies are that there is no enough information regarding dose-response relationship and the relevance of the biological target.

All these challenges were the reason for initiatives to change the way clinical trials are designed.

What kind of new approaches could be used?

  • One of the new approaches involved combination of different types of modelling: biological modelling, pharmacological modelling and statistical modelling. This method allows to use external data in order to model and simulate your study. Later on simulation would allow selecting the best study design to achieve your goals.
  • Bayesian modelling – this method combine external baseline data to improve efficacy and safety signal detection in early development. An example of such approach is to use small cohorts with dose-escalation to assess efficacy and safety at the end of each cohort. Dose will be increased until maximum therapeutic dose is achieved or unacceptable toxicity. Each new cohort will start only if all safety parameters are assessed for the different doses from the previous cohorts. This will allow detecting safety problems which are observed in large population of patients and only relatively strong efficacy signals will be detected.
  • Adaptive designs – In this case interim data from a trial is used to modify and improve the study design, in a pre-planned manner and without undermining its validity or integrity. For example, a larger proportion of the enrolled patients can be assigned to the treatment arms that are performing well, drop arms that are performing poorly, and investigate a wider range of doses so as to more effectively select doses that are most likely to succeed. In the next stage this allows to identify early efficient treatment, drop poorly performing arms, stop the study early for meeting its primary endpoints or modify eligibility to include more patients.
  • Seamless designs – in this case single trial has objectives that are traditionally addressed by separate trials.
  • Sample size re-estimation methods – this allows to increase or decrease sample size at an interim point for the trial.

The way we run clinical trials is changing and while it becomes more flexible in drug development point of view it could also become more complex from research centres point of view.


The future of drug development: advancing clinical trial design

Published on 6 Dec 2018

Author: Olga Peycheva, Director at Solutions OP Ltd. 
Olga has been working in clinical research since 2005 and has extensive experience in Eastern and Western Europe