Our First Pre-Registration is Live! Replication of…

After months of efforts, my co-authors and I are absolutely delighted to share this preprint, which is special in many ways:

Said, Maha, Mustafa Gharib, Samia Zrig, and Raphaël Lévy. 2023. “Replication of “Carbon-dot-based Dual-emission Nanohybrid Produces a Ratiometric Fluorescent Sensor for in Vivo Imaging of Cellular Copper Ions”” OSF Preprints. November 29. doi:10.31219/osf.io/kf9qe.

This preprint is special because it does not contain any data*: it is a pre-registration of a study. This means that what you will read is not a selection of results assembled to tell a nice story, but our plans to test experimentally a series of hypothesis. We are submitting these plans for peer review, both formal (through PCI RR) and informal (everyone is invited to comment at PubPeer). This makes so much more sense than the traditional peer review system: by peer reviewing our proposed plans and methodology you can truly help us build a more robust study that will contribute to solve the paradox of intracellular sensing with nanoparticle probes and help establish standards in how to study endosomal escape of nanoparticles. Once the pre-registered report receives “In Principle Acceptance”, after one or more rounds of peer review, we will do the experimental work, following the registered protocol, and the results will be published whatever they are. So, not only does this approach helps achieve a sound methodology before the experiments starts, it also helps to solve the problem of publishing bias where “negative results” don’t get published thus distorting the literature.

This preprint is also special because it is the first public step in the ERC NanoBubbles replication project in which we hope to reproduce several highly cited articles that report intracellular sensing with nanoparticles. We will also use this mechanism of pre-registration of studies for the next replications.

Now, I am sure you are wondering how you can help? The good news is that there are many ways. Read our registered report. Share this post to give visibility to this initiative. Peer review the proposal and give us constructive feedback to improve our plans. Get in touch to help us with the next pre-registration where we want to do a multi-site replication and will therefore need partners (nanoparticle synthesis, characterisation, microscopy, image analysis).

I am incredibly grateful to Maha and Mustafa who have done most of the work preparing this document; to Samia who supervised Mustafa for a little bit of organic synthesis (the only bit that we have done pre-registration; see paper for details). We are also thankful to the European Research Council for funding the project, and to Nicole Hondow (University of Leeds) and Aurélien Deniaud (University of Grenoble) for their suggestions and comments on the manuscript.

Editors and scientific journals are reluctant to correct the scientific record; episode 999

In the context of the post-publication peer review initiative of the NanoBubbles project, we posted a detailed comment at PubPeer on Two-Photon Ratiometric Fluorescent Sensor Based on Specific Biomolecular Recognition for Selective and Sensitive Detection of Copper Ions in Live Cells; Analytical Chemistry (2013).

We also contacted the Editor-in-Chief because some of the findings were suggestive of image manipulation.

Like, for exemple, the same cell twice in the same field of view (pink squares).

The Editor-in-Chief contacted the authors, who argued that there was no image manipulation. I am not authorised to reproduce their argument that, unfortunately, they have not posted at PubPeer. The editor found the explanation reasonable and closed the case.

I responded to the Editor-in-Chief the 17/08. I am yet to hear back.

Dear Jonathan,

Thank you for considering this matter and for contacting the authors. I have to admit that I am rather surprised by the arguments offered in response and the conclusion that was reached.
One could point to the fact that doing different treatments on the actual same cells is neither what is described in the article nor what should be done (because treating with A, then with B, then with C, is obviously not the same as comparing treatments by treating with A or B or C). One could also remark that it is not possible that live cells don’t move at all for more than one hour (A and E).

However, there is no need for those slightly subtle arguments.

The most obvious observation that cannot be explained by anything else than an image manipulation is the presence of the same cell twice in the same image (Fig 4, panel G, pink squares). You will note that the two cells are identical down to the most minute details and that they also have exactly the same orientation. I do not believe that any scientist who has worked with cells would find this plausible.

Then, there is the comparison between E and G. The cells in the green rectangle have been rotated by exactly 90 degrees between the two images… but the cells in the red circle have not been rotated between the two images. So the authors are asking us to believe that some cells have not moved at all nor changed shapes, but some have rotated by 90 degrees while keeping their exact same shapes as well as relative positions to one another (but not to those in the green rectangle, obviously). Some have also mysteriously appeared or disappeared. Obviously washing or microscope movement cannot explain those peculiarities.

This is not serious.

I invite you to reconsider and I am looking forward to hear back from you.

Regards,

Raphaël

Gold injections – how to use the scientific literature to sell snake oil to patients

To know more about the adventures of Dr Doxey, an unscrupulous charlatan ready to do anything to sell his worthless elixir, read the Lucky Luke Western album by Morris.

To know more about Goldic, a real story that does not happen in Lucky Luke’s imagined Wild West, but in the present time, in the UK, Germany and possibly other places, where doctors will take your money in exchange of a miraculous therapy where they will draw your blood and re-inject it after it has been incubated with gold nanoparticles, read Private Eye’s investigation featuring expert and former colleague Patricia Murray (and a quote from me too):

Khan made even wilder claims to a prospective patient: “It gets your body working as it did in your 20s. You will find yourself having more energy, you’ll be quicker in the way that you think – all those things are going to improve.

All of which is quackery, says Patricia Murray, professor of stem cell biology at Liverpool University. She told the Eye: “I am very concerned that this is being promoted as the next generation of stem cells. There is no evidence to support these claims. It seems patients are, once again, being exploited for financial gain.”

If you are in the UK, go and buy your copy in your local newsagent, or subscribe. For the others, I will, with the Eye’s generous authorisation, update this post and share the full article once the next issue of the Eye is out.

In the meantime, you can read a related PubPeer comment. Dr Doxey claimed the efficacy of his elixir based on “years of tireless research”. Present days charlatans mis-use the peer reviewed scientific literature to prove it.

Guest Post: Sensing by Surface Enhanced Raman Scattering (SERS) : to the Moon and back down to earth again

This is a guest post by Gaëlle Charron, Maîtresse de conférences at Université de Paris.

I was about to submit a paper about the detection of atomic ions by SERS the other day. The paper had been in the pipeline for months. I went through a last survey of the recent literature to check for fresh references that it would have been unfair to leave out. When I bumped into a 22 pages review just about that: Examples in the detection of heavy metal ions based on surface-enhanced Raman scaterring spectroscopy.

It sucks, I thought, as cold sweat was pouring down my neck. I have been working on this “novel” idea for 9 years now. Revisiting the dyes developed as colorimetric indicators for the detection of metal ions through a SERS angle. SERS sensors exploiting not the absorption properties of the indicators, but their vibrational signatures. The first time I thought about it sometime in 2012, I was excited as a blinking Christmas tree. The literature about colorimetric quantification of metal ions, mainly from the 40’s, 50’s and 60’s was rich, reliable and pretty informative. Lots of options for commercial indicators, lots of experimental details, many of them put to use in classic lab courses. And above all, lots of thermodynamic constants to use to emulate the chemical system. It felt like I could do nano properly, to the quantitative standards of textbook chemistry. Obviously, many people would see the same opportunity, as every undergraduate chemistry student will have played with these complexometric indicators at one point or another in her curriculum. About a month after my initial epiphany, I discovered that Luis Liz-Marzàn had already killed the game. An ACS Nano paper about ultrasensitive chloride detection, in the pM range. And now a full review was out, with its 80 examples of metal ion detection by SERS. I was late.

Feeling moody, I dived into the review. A general introduction about how deleterious and ubiquitous metal contamination is, about how heavy metals are usually quantified and about the limitations of those methods. The classic primer about SERS and its accepted mechanisms. And then, metal target by metal target, examples of dedicated SERS sensors.

In the general introduction, a sentence caught my attention. More and more researchers have used SERS technology to detect and quantitatively or semiquantitatively analyse heavy metal ions in various environments. That sentence cites a paper of mine about the setting-up of a SERS sensor of Zn²⁺ in pure water, ie. in the simplest of matrices, in a lab environment. Like in the other cited references associated with that sentence, my team did not use SERS technology to quantify a metal contaminant in the environment. We just examined the possibility of quantifying that contaminant with SERS. Much like examining the effectiveness of a drug to treat a disease does not mean that the drug is used to cure the disease.

Why does it make a big difference? For one, for the sake of accuracy. Then because there are many shortcomings to developing any new chemical analysis method. Is the sensitivity appropriate for the concentration range in which the target analyte will likely be encountered? Are the readouts true enough, precise enough? How much time, effort and money does it take to produce a readout? How likely is the method to work every day of the week when we switch the spectrometer on or open the fridge to reach for a nanoparticle batch? All of the above compared to the standard analytical methods? All of the above when analysing a typical specimen of the targeted samples and matrices?

The authors of the review acknowledged, at least partially, those potential pitfalls. Summary tables of examples of detection were given for each metal ions with ranges of linear response to analyte concentration, LODs and comments. The latter listed the following adjectives: sensitive, accurate, anti-interference, reliable, complicated, selective, low sensitivity, simple, rapid, low reproducibility. Yet, at no point in the review is the practicality of the reviewed SERS sensors discussed in comparison with the standard methods used by people actually performing chemical analysis of contaminants. It seems like those people were never consulted. Like the mad nano-scientists and the end-users were never put in the same room with a tea trolley.

The simplest illustration of this appears in the reported LODs and sensitivity ranges, for instance in the case of mercury quantification. The maximum concentration in drinking water set by the US Environmental Protection Agency is 10 nM. The concentration range for mercury in drinking-water is the same as in rain, with an average of about 125 pM. Naturally occurring mercury concentration in groundwater is less than 2.5 nM. Yet Table 2 of the review lists a sensitivity range of 10 fM to 100 pM, fully irrelevant to flagging an abnormal concentrations in drinking water, rain water or groundwater, or several sensitivity ranges unsuitable to assess the safety of drinking water (9.97 pM-4.99 nM, 4.99 pM-2.49 nM).

The focus of the description of those sensors is on the chemical schemes put to use, many of which sound rather overhyped.

Wang et al. created a dual signal amplification strategy based on antigen–antibody reaction to recognize copper ions (Figure 4c) [78]. Specifically, they started with decorating the multiple antibiotic resistance regulator (MarR) that worked as bridging molecules and 4-MBA served as a Raman reporter on the surface of AuNPs, and then Cu²⁺ions generated disulfide bonds between the two MarR dimers by oxidizing cysteine residues, which induced the formation of the MarR tetramers, leading to the aggregation of AuNPs and the reinforcement of the SERS signal of 4-MBA. In the meantime, another substrate, AgNPs capped with anti-Histag antibodies combined with MarR (C-terminal His tag) to constitute dual hot spots and the reticulation of AuNP–AgNP heterodimers. The dramatic signal enhancement allowed the detection limit to reach 0.18 nM with a linear response in the range of 0.5–1000 nM.

Take a deep breath. And a drink. The smart mouth contest goes on and on.

Without quite a full chemical legitimacy I would say. In the previous example, you might have felt an itch. Let’s rewind and replay.

The dramatic signal enhancement allowed the detection limit to reach 0.18 nM with a linear response in the range of 0.5–1000 nM.

The range onto which the sensor responds linearly, onto which the signal vs. concentration calibration model will be built, is 0.5-1000 nM. Yet a LOD of 0.18 nM is claimed. What happens to the signal dependence on the concentration below 0.5 nM is either too noisy or too flat to enter the calibration model or it has simply not been tested. Yet, the sensor is claimed to be operational at a concentration within this unchartered territory. Out of the 13 entries in the table dealing with mercury sensing, 9 displays a LOD below the lower limit of the sensitivity range. Error is not incidental here, it is the norm.

Also, as an undergraduate, I had learnt that an indicator abruptly changes speciation at an analyte concentration on the order of the dissociation constant of the indicator-analyte complex. A sensitivity in the pM would call for a 10^-12 dissociation constant, a magnitude that is only encountered with chelating ligands with many binding atoms and/or at high pH, conditions that were not discussed, and very seldom met in the reviewed examples. But that may be the object of a full discussion in itself: does anyone understand how the sensing actually occurs, I mean beside our fantasized chemical sketches?

I still went through the full review. The conclusion nearly had a point, too bad they did not think it was worth an actual discussion.

At present, SERS technology basically stays as a laboratory test, which still has a big challenge for the quantitative testing on-site and actual complex samples, so it cannot be regarded as one of the conventional detection assays.

(Damn right it isn’t. I have been chasing my own tail for 9 years.)

Closing the paper print, an image came to my mind. That of a father of three kids going to a car dealership, looking for a vehicle that could accommodate three child safety seats. To which the dealer presents a half-assembled Lamborghini.

–          Preliminary tests indicates that it can go to 200 km/hr in 10 s. It will have a DVD screen on the passenger side.

–          I doubt the 3 car seats will fit at the back.

–          Leather seats are included.

–          It is half-finished. Plus it has breadcrumbs all over and a large crack into the windshield.

–          Yeah. You might want to fix that before you drive with the kids.

Lamborghini Model SERS – Credit to Nathanaël Lévy (13 years old)

Do nanoparticles cross membranes? Further thoughts on “Time of entry of nanoparticles in lipid membranes”

This blog article has benefitted from discussions with philosopher Yasemin J Erden. The analysis presented here was originally part of a Perspective article co-written with her and focused on the question of how wrong beliefs (such as diffusion of nanoparticles through membranes) persist for a long time. We have decided to separate the detailed critique of Liu et al’s article (below) from the general argument about persisting wrong beliefs. The Perspective article is currently submitted to a scientific journal and already available as a preprint.

I have discussed previously the article by Liu et al (here). However, I have since given a lot more thought to two points which had been troubling me: 1) given that there is no convincing experimental evidence that diffusion of nanoparticles across lipid membranes exists, how do the authors make the case for their theoretical study? 2) given that they include a short experimental section: how do they compare their theory with the experimental results?

HOW LIU ET AL MAKE THE CASE TO STUDY DIFFUSION OF NANOPARTICLES THROUGH MEMBRANES

They start by noting that some small organic compounds can diffuse through lipid membranes but that this is not the case for polar macromolecules. Then they make a general case for the importance of nanomaterials in biological applications. In table 1 below, I reproduce short statements from the Liu et al’s introduction which are supported by citations. I contrast what is likely to be understood by the readers with what is actually shown in the articles cited. This analysis reveals significant hype, but also the interesting fact that the claim for applications is made by citing, not articles reporting the existence of applications in biology and medicine, but instead chemistry articles reporting on the development of nanoparticles and making promises of future applications.

Table 1 also shows that, even though diffusion through membranes is the phenomenon they are modelling, none of the experimental articles cited in their introduction claim that nanoparticles diffuse through membranes. Instead, most cited articles report (correctly) entry of nanoparticles into cells by endocytosis. The erroneous claim that nanoparticles with one dimension smaller than 10-15 nm can enter by “diffusing in the hydrophobic region of the membrane and then on the other side” is made without reference. The introduction then moves to the review of theoretical and computational articles studying diffusion of nanoparticles through membranes. Thus, the case for studying the non-existing phenomenon of nanoparticles diffusion through membranes is a succession of four claims:

1. Some small molecules diffuse through membranes.
2. Nanoparticles have all sorts of important applications in biology and medicine (mostly, promises of).
3. Small nanoparticles can diffuse through membranes (they don’t).
4. All those previous theoretical efforts at studying diffusion of nanoparticles through membranes have shortcomings so more research is necessary.

Claim 1 gives a credible foundation. Claim 2 establishes importance. Claim 4 is that we do not have a good enough understanding of the (non-existing) phenomenon.  We suggest that similar sequences of claims above are common among bionano articles. This structure of argument is indeed quite similar to the one that can be found in the first molecular dynamics simulation of the (non-existing) diffusion of gold nanoparticles through membranes 10 years ago.

(scroll down and read on after the table for the section on comparison between theory and experiments; you can download the table in an easier to read format here)

Table 1: How Liu et al justify their study of the nanoparticles-diffuse-across-membranes (non-existing) phenomenon. References noted in brackets, e.g. [2-4], refer to Liu et al own bibliography.

Quotes from Liu et al introduction.	This may give the impression that:	Comment:	Relevance of the cited experimental work to the question of diffusion of nanoparticles across membrane:
Tunable […] properties of […] nanomaterials enable engineering for a number of applications, such as drug delivery,[2−4] …	Nanomaterials are enabling drug delivery, or may do so soon.	Nanomaterials enable engineering for applications, but the cited articles are chemistry research articles making promises for the future. Ref 2 is so far from drug delivery that it does not even have cell biology experiments. Ref 3-4 are also mostly materials synthesis and characterization (some basic cell biology, no preclinical work).	Ref 2: None. Ref 3: None. The article report that the particles enter by endocytosis. They are supposed to escape later. No evidence, quantification or mechanism provided. Ref 4:  None. The article reports that the particles enter by endocytosis.
Tunable […] properties of […] nanomaterials enable engineering for a number of applications, such as […], controlled release,[3,5]…	Nanomaterials are enabling controlled release, or may do so soon.	Ref 3, see above. Ref 5 is a highly-cited 2008 JACS communication. Mostly materials synthesis and characterisation with one figure showing cell viability and toxicity. 12 years later, the medical application of these materials (PEGylated Nanographene Oxide) does not seem any closer.	Ref 3: None. See above.   Ref 5: The article reports that the particles enter by endocytosis.
Tunable […] properties of […] nanomaterials enable engineering for a number of applications, such as […], deep tissue imaging, and sensing of cellular behavior.[6−10]	Biologists and medical doctors are using those nanomaterials for deep tissue imaging, and sensing of cellular behaviour, or may do soon.	Ref 6-10 are chemistry papers largely describing the synthesis of new materials which could, according to their authors, be useful for deep tissue imaging etc. Some of these are 5+ years old but no biologist currently use these materials for their imaging or sensing needs.	Ref 6: No comment on mechanisms of uptake. Ref 7: None. See also PubPeer comment.9 Ref 8: None. The article reports receptor-mediated endocytosis. Ref 9: None. Ref 10: None.
Nonetheless, over the years, a number of systems have been reported to give cytoplasmic access to biomacromolecules, most notably cell-penetrating peptides,[17] supercharged proteins,[18,19] and bacterial toxins and different types of NPs.[20−22]	Those strategies are an effective strategy for cytoplasmic access.	In fact, effective delivery to the cytosol remains a highly challenging task as some of these papers make clear. In the rare cases where cytosolic delivery is quantified, authors find that the proportion of nanomaterials reaching the cytosol is very small.10 That is also our experience, e.g. with TAT.11	Ref 17: None. Entry into cells is by endocytosis. Endosomal escape is supposed to involve specific membrane disruption mechanisms. Ref 18: None. Entry into cells is by endocytosis. Endosomal escape is supposed to involve membrane fusion between the lipid-coated “super-charged protein” and the target cell. Ref 19: None. Entry into cells is by endocytosis. Endosomal escape is via a protein pore. Ref 20. None. It is a 20 years old review of synthetic DNA Delivery Systems. Ref 21: None. It is a review of extracellular vesicles for drug delivery. It includes very little on mechanisms of uptake and no suggestion it could be by diffusion through the membrane. Ref 22: None. It is a review of nanoparticulate drug delivery system. In the section on accessing intracellular targets, there is no mention of any potential role of diffusion through membranes.
For particles with the smallest dimension larger than the membrane thickness, approximately above 10−15 nm, the permeation is generally controlled by membrane deformation [23] and endocytosis [24].	General rules about what controls permeation for “particles with the smallest dimension above 10-15 nm” have been obtained.	Multiple levels of confusion. Ref 23 is about graphene sheets; their smallest dimension is well below 10-15 nm (and there is no reason to extrapolate any general rule from that particular study). Ref 24 is about endocytosis but the smallest dimension of the smallest particle tested is 100 nm so it cannot tell anything about a supposed threshold around 10-15 nm.	Ref 23: None. The experimental section of the article uses graphene oxide sheets, which are 200 to 700 nm in lateral size. There is no experimental evidence that such objects could diffuse through membranes. Ref 24: None. Entry into cells is indeed by endocytosis.
Smaller nanoparticles can instead cross the membrane by passive transport, that is, by displacing, sometimes irreversibly, the lipids or by diffusing in the hydrophobic region of the membrane and then on the other side.	Particles with the smallest dimension smaller than 10-15 nm can cross membranes by passive transport.	No reference provided for this essential claim. There is no serious evidence for its validity – and there is plenty of conflicting evidence. In fact, if true, most proteins would diffuse through membranes, which is obviously not the case.	N/A

THE MODEL AND THE COMPARISON WITH EXPERIMENTS

Liu et al start building their model from an observation. But not an observation from physical reality. They observe that in a molecular dynamics time series of a nanoparticle close to a lipid membrane, nanoparticle penetration into the membrane is happening at the same time as a low-density fluctuation of the lipids. From that observation, they build an ad hoc phenomenological theoretical model composed of four mechanistic steps. Those four steps are not illogical, but they are also not the only possible ad hoc description available (e.g. a fifth step could be added accounting for the rotation of the nanoparticle in the correct orientation that matches the low density area). How those steps are transcribed into mathematical equations and then into predictions also involves several somewhat arbitrary decisions (e.g. a density threshold defining what is a ‘low density area’ and a decorrelation threshold defining a decorrelation time). This results in underdetermination, i.e. even if the model were in agreement with experimental results, it would not give much confidence that it really describe the physical reality.

In contrast to many theoretical articles, Liu et al do include experimental results. Liu et al’s model predicts, as the title of the article indicates, the “time of entry of nanoparticles in lipid membranes”. But what does ‘time of entry’ actually mean? How is the model tested experimentally?

Permeability, not time of entry, is usually measured to characterise transport through a membrane. The permeability  relates the flux  of a substance (permeant) through a membrane to the difference in the permeant’s concentration on either sides of the membranes¹:

The permeability depends on the permeant and membrane properties. The difficulty with time of entry is that whilst it may seem to have an intuitive meaning, it is not a well-defined physical parameter: it does not just depend on the permeant and membrane properties. Instead, the time a nanoparticle takes to enter a membrane (or more precisely the statistical distribution of the times of entry) will depend on the starting position of the nanoparticle, and in particular its distance from the membrane. In Liu et al’s molecular dynamics simulations, the nanoparticles are confined within 2 nm of the membrane by ‘a harmonic potential that pushes the particles toward the membrane when its distance from the membrane interface exceeded 2 nm’. Altering or removing this confinement would dramatically alter the time taken by a nanoparticle to enter the membrane. By reading very carefully Liu et al, one can conclude that the quantitative definition of what they call time of entry Tentry is in fact as follows: molecules or nanoparticles confined within 2 nm of the membrane will penetrate that membrane with an average rate of 1/Tentry . 

To compare with other models or with experiments, one needs to relate this rate with permeability.  Here, we face two problems: 1) the fact that Liu et al’s model only considers the first half of the process, i.e. the entry of nanoparticles into membranes, whilst permeability relates to the passage of permeants through the membrane, i.e. the entry and the escape; 2) the need for a quantitative relationship between the rate of passage and the permeability. Thankfully, both problems can be solved as we will show below.

To address the first problem, one can easily extend Liu et al’s model by applying their approach to the escape. Liu et al’s model is shown in Figure 1 (top). The nanoparticles they studied are highly lipophilic. They report Pt, the probability of the NP to move from the hydrophilic phase to the hydrophobic phase, as being equal to 100% in all cases (table 1 and 2 in Liu et al). In such a scenario, if a particle encounters an LDA, they will always enter as their affinity for the lipid phase is infinite. But, with Pt equal to 100%, particles would never escape from the membrane (Figure 1, bottom): they would just accumulate in the lipid phase as the probability of escape from the lipid phase would be 0. In a private communication, the authors have provided a finite value for the partition coefficient Kd (that they deem appropriate for the particles used in their experimental validation). This value can be used to calculate Tescape, the time of escape using exactly the same reasoning as Liu et al (Table 2). One can observe that for such lipophilic particles, the time of escape is three orders of magnitude larger than the time of entry (therefore a temporary accumulation in the membrane should be observed which does not seem to be the case from the images shown in the article).

Figure 1: Extending Liu’s model to consider also the escape of nanoparticles from the membrane. Top: Liu et al propose a gated entry model where particles colliding with the membrane penetrate with a probability Pt into the lipid phase if they encounter a low density fluctuation of lipids or LDA. The probability Pt depends on the partition coefficient Kd of the NP in water/lipid: Pt = Kd/(1+Kd). There is a probability Plda of encountering such a LDA. Bottom: We extend this model by applying an identical reasoning to the escape of nanoparticles from the membrane.

Finally, to compare between the model, the extended model, and the experiments, one needs to convert the rate of entry, or rate of passage, into a permeability. If one considers a short time interval delta-t, the amount of nanoparticles crossing an area  of the membrane is:

The amount of nanoparticle crossing an area A of the membrane in a short time interval delta-t can also be obtained by calculating the number of particles at a distance smaller than h =2 nm, i.e.  A*h*Cin and multiplying that number by the rate of entry:

By comparing equations (2) and (3) above, one can deduce that there is a simple relationship between the permeability and the time of entry:

Using equation 4, one can now compare the results of the experiments of Liu et al and the predictions from the models (Table 3). The experiments were done for three membrane compositions and one type of nanoparticles. The permeability was measured by quantifying the leakage of the nanoparticle from a spherical vesicle. First, one can remark that there is a very large uncertainty over the permeabilities measured by Liu et al for the first two membrane compositions, to the extent that they are not significantly different from each other and not significantly different from zero either. This large uncertainty is due mostly to the fact that over the course of the experiment (60 minutes), only a small percentage (less than 20%) leakage is observed for these two membrane compositions, and, combined with the data variability, it is not possible to measure an exponential decay with any confidence. Second, one can remark that the entry-only model predicts values which are 6 orders of magnitude larger than the observed permeability (the predicted values are larger than what has been observed for water – clearly unrealistic for nanoparticles). Third, the extended model predicts values that are still three orders of magnitude too large. The conclusion therefore should be unambiguous: the model’s predictions do not match with physical reality. This is however not the conclusion reached by the author.

Table 3: Comparison between the models and the experiments.

P/C	Permeability measured by Liu et al (Fig 9b of Liu et al), nm/s	Permeability deduced from the entry-only model (nm/s)	Permeability deduced from the extended model (nm/s)
50:50	0.2 ± 0.7	3.8 105	120
70:30	1.0 ± 1.8	1.8 106	579
90:10	3.9 ± 0.7	5.9 106	1860

Liu et al do not discuss the fact that their model only describes entry into the membrane and not the escape. They do not discuss the very large uncertainty of two of their measured experimental permeabilities. They do not deduce an absolute relation between rate of entry and permeability. Instead, they postulate the following equation which has the same form as equation (4):

They then calculate c from a fit of the experimental data (Liu et al, private communication). Here we see the effects of underdetermination in practice. Liu et al bundle what they have not modelled, about which there is uncertainty or a lack of knowledge, into a constant. This in turn means that the constant is therefore unavoidably vague. And its value is determined by a fit of the data that are supposed to test the model. Unsurprisingly they obtain ‘a very close match’ and they conclude that the experiments validate their approach.

1.           Philips, R. & Milo, Ron. » What is the permeability of the cell membrane? http://book.bionumbers.org/what-is-the-permeability-of-the-cell-membrane/.

2.           Ioannidis, J. P. A. Contradicted and Initially Stronger Effects in Highly Cited Clinical Research. JAMA 294, 218 (2005).

3.           Fanelli, D. “Positive” Results Increase Down the Hierarchy of the Sciences. PLOS ONE 5, e10068 (2010).

20 critical reviews of influential articles about nanoparticles and cells

I have commented on the 20 highly cited articles below. They all relate to nanoparticles and cells. They were published between 1998 and 2006 and have received more than 1,000 citations each, over 40,000 citations overall.

I have used Twitter to document my reviewing process.

In this thread, I will be looking back at highly cited (over 1000 citations) ‘historical’ (prior to 2008) bionano papers, in particular those that deal with nanoparticles & cells. Comments most welcome. #bionano #NanoBubble

— (((Raphael Levy))) (@raphavisses) February 6, 2019

I have copied all of my reviews to PubPeer ; see the link below each papers in the bibliography at the bottom of this post. The orange colour indicates serious problems; the blue colour indicates that important old relevant papers have been overlooked.

You can also find the tweets via the ThreadReaderApp:

• Intro and articles 1 to 10.
• Articles 11 to 17
• Articles 17 (continued) to 20

 

1             Bruchez, M., Moronne, M., Gin, P., Weiss, S. & Alivisatos, A. P. Semiconductor nanocrystals as fluorescent biological labels. Science 281, 2013-2016, doi:10.1126/science.281.5385.2013 (1998).

=> Comment on PubPeer.

2             Gref, R. et al. ‘Stealth’ corona-core nanoparticles surface modified by polyethylene glycol (PEG): influences of the corona (PEG chain length and surface density) and of the core composition on phagocytic uptake and plasma protein adsorption. Colloids and Surfaces B-Biointerfaces 18, 301-313, doi:10.1016/s0927-7765(99)00156-3 (2000).

=> Comment on PubPeer.

3             Lewin, M. et al. Tat peptide-derivatized magnetic nanoparticles allow in vivo tracking and recovery of progenitor cells. Nature Biotechnology 18, 410-414, doi:10.1038/74464 (2000).

=> Comment on PubPeer.

4             Akerman, M. E., Chan, W. C. W., Laakkonen, P., Bhatia, S. N. & Ruoslahti, E. Nanocrystal targeting in vivo. Proceedings of the National Academy of Sciences of the United States of America 99, 12617-12621, doi:10.1073/pnas.152463399 (2002).

=> Comment on PubPeer.

5             Hirsch, L. R. et al. Nanoshell-mediated near-infrared thermal therapy of tumors under magnetic resonance guidance. Proceedings of the National Academy of Sciences of the United States of America 100, 13549-13554, doi:10.1073/pnas.2232479100 (2003).

=> Comment on PubPeer.

6             Lai, C. Y. et al. A mesoporous silica nanosphere-based carrier system with chemically removable CdS nanoparticle caps for stimuli-responsive controlled release of neurotransmitters and drug molecules. Journal of the American Chemical Society 125, 4451-4459, doi:10.1021/ja028650l (2003).

=> Comment on PubPeer.

7             Wu, X. Y. et al. Immunofluorescent labeling of cancer marker Her2 and other cellular targets with semiconductor quantum dots. Nature Biotechnology 21, 41-46, doi:10.1038/nbt764 (2003).

=> Comment on PubPeer.

8             Gao, X. H., Cui, Y. Y., Levenson, R. M., Chung, L. W. K. & Nie, S. M. In vivo cancer targeting and imaging with semiconductor quantum dots. Nature Biotechnology 22, 969-976, doi:10.1038/nbt994 (2004).

=> Comment on PubPeer.

9             Sondi, I. & Salopek-Sondi, B. Silver nanoparticles as antimicrobial agent: a case study on E-coli as a model for Gram-negative bacteria. Journal of Colloid and Interface Science 275, 177-182, doi:10.1016/j.jcis.2004.02.012 (2004).

=> Comment on PubPeer.

10           Connor, E. E., Mwamuka, J., Gole, A., Murphy, C. J. & Wyatt, M. D. Gold nanoparticles are taken up by human cells but do not cause acute cytotoxicity. Small 1, 325-327, doi:10.1002/smll.200400093 (2005).

=> Comment on PubPeer.

11           El-Sayed, I. H., Huang, X. H. & El-Sayed, M. A. Surface plasmon resonance scattering and absorption of anti-EGFR antibody conjugated gold nanoparticles in cancer diagnostics: Applications in oral cancer. Nano Letters 5, 829-834, doi:10.1021/nl050074e (2005).

=> Comment on PubPeer.

12           Hussain, S. M., Hess, K. L., Gearhart, J. M., Geiss, K. T. & Schlager, J. J. In vitro toxicity of nanoparticles in BRL 3A rat liver cells. Toxicology in Vitro 19, 975-983, doi:10.1016/j.tiv.2005.06.034 (2005).

=> Comment on Pubpeer.

13           Kirchner, C. et al. Cytotoxicity of colloidal CdSe and CdSe/ZnS nanoparticles. Nano Letters 5, 331-338, doi:10.1021/nl047996m (2005).

=> Comment on PubPeer.

14           Loo, C., Lowery, A., Halas, N. J., West, J. & Drezek, R. Immunotargeted nanoshells for integrated cancer imaging and therapy. Nano Letters 5, 709-711, doi:10.1021/nl050127s (2005).

=> Comment on PubPeer.

15           Morones, J. R. et al. The bactericidal effect of silver nanoparticles. Nanotechnology 16, 2346-2353, doi:10.1088/0957-4484/16/10/059 (2005).

=> Comment on PubPeer.

16           Chithrani, B. D., Ghazani, A. A. & Chan, W. C. W. Determining the size and shape dependence of gold nanoparticle uptake into mammalian cells. Nano Letters 6, 662-668, doi:10.1021/nl052396o (2006).

=> Comment on PubPeer.

17           Huang, X. H., El-Sayed, I. H., Qian, W. & El-Sayed, M. A. Cancer cell imaging and photothermal therapy in the near-infrared region by using gold nanorods. Journal of the American Chemical Society 128, 2115-2120, doi:10.1021/ja057254a (2006).

=> Comment on PubPeer.

18           Panacek, A. et al. Silver colloid nanoparticles: Synthesis, characterization, and their antibacterial activity. Journal of Physical Chemistry B 110, 16248-16253, doi:10.1021/jp063826h (2006).

=> Comment on PubPeer.

19           Rosi, N. L. et al. Oligonucleotide-modified gold nanoparticles for intracellular gene regulation. Science 312, 1027-1030, doi:10.1126/science.1125559 (2006).

=> Comment on PubPeer.

20           Xia, T. et al. Comparison of the abilities of ambient and manufactured nanoparticles to induce cellular toxicity according to an oxidative stress paradigm. Nano Letters 6, 1794-1807, doi:10.1021/nl061025k (2006).

=> Comment on PubPeer.

 

 

How to Elucidate the Structure of Peptide Monolayers on Gold Nanoparticles?

I have recently submitted my PhD thesis and we have now pre-printed on bioRxiv the work constituting its major chapter. Together with the pre-print, the data have been made publicly available in an online repository of the University of Liverpool. Well isn’t it perfect timing that this week is open access week? 😉

This work has been conducted nearly entirely during the 2 years of my PhD spent at the A*STAR Institute of Materials Research and Engineering (IMRE) and at the A*STAR Institute of High Performance Computing (IHPC) in Singapore.

In this study, peptide-capped gold nanoparticles are considered, which offer the possibility of combining the optical properties of the gold core and the biochemical properties of the peptides.

In the past, short peptides have been specifically designed to form self-assembled monolayers on gold nanoparticles. Thus, such approach was described as constituting a potential route towards the preparation of protein-like nanosystems. In other words, peptide-capped gold nanoparticles can be depicted as building-blocks which could potentially be assembled to form artificial protein-like objects using a “bottom-up” approach.

However, the structural characterization of the peptide monolayer at the gold nanoparticles’ surface, essential to envision the design of building-blocks with well-defined secondary structure motifs and properties, is poorly investigated and remains challenging to assess experimentally.

In the pre-printed manuscript, we present a molecular dynamics computational model for peptide-capped gold nanoparticles, which was developed using systems characterized by mean of IR spectroscopy as a benchmark. In particular, we investigated the effect of the peptide capping density and the gold nanoparticle size on the structure of self-assembled monolayers constituted of either CALNN or CFGAILSS peptide.

The computational results were found not only to well-reproduce the experimental ones, but also to provide insights at the molecular level into the monolayer’s structure and organization, e.g., the peptides’ arrangement within secondary structure domains on the gold nanoparticle, which could not have been assessed with experimental techniques.

Overall, we believe that the proposed computational model will not only be used to predict the structure of peptide monolayers on gold nanoparticles, thus helping in the design of bio-nanomaterials with well-defined structural properties, but will also be combined to experimental findings, in order to obtain a compelling understanding of the monolayer’s structure and organization.

In this sense, we would like to stress that, in order to improve data reproducibility, enable further analysis and the use of the proposed computational model for peptide-capped gold nanoparticles, we are making the data and the custom-written software to assemble and analyse the systems publicly available.

Snapshots of the final structure of the simulated 5 (left) and 10 (right) nm CFGAILSS-capped gold nanoparticle, illustrating different content and organization of secondary structure motifs.

Is targeting your target?

Warren Chan’s group published in June a perspective in Nature Reviews Materials entitled “Analysis of nanoparticle delivery to tumours” (Wilhelm et al). A key finding of their analysis of the literature is the absence of increase in the (very small) amount of nanoparticles delivered to tumours in the past 10 years. In a welcome departure from the usually overly diplomatic and confused style that is the trademark of most scientific writing, Wilhelm et al write the following:

 “These advantages [of nanoparticles] have been dampened by the lack of translation to patient care, despite the large investment (more than $1 billion in North America in the past 10 years) and success in imaging and treating tumours in mouse models. As a result, nanomedicine has acquired a reputation of being “hype” that cannot deliver and has not transformed patient care as it promised 15 years ago”

[…]

“We must admit that our current approach is broken, and that is why we have not observed significant clinical translation of cancer nanomedicines. Many academic studies focused on the potential of nanoparticles for in vivo applications and showed that nanoparticles may be delivered to tumours by the EPR effect. However, publishing ‘proof of concept’ studies will only lead to curing mice and will unlikely translate to cancer care, irrespective of the number of nanoparticle design permutations used for cancer targeting studies.”

Recognising the magnitude of the challenge, Wilhelm et al propose a thirty year strategy for nanomedicine.

Not surprisingly the publication sparked a debate; see for example Derek Lowe’s blog “Nanoparticles Mix It Up With Reality” and the comments therein, and the article by Michael Torrice for Chemical and Engineering News “Does nanomedicine have a delivery problem?” which features a number of quotes by various nanomedicine players, some of whom contesting Wilhelm et al’s findings, or their relevance to the development of nanomedicine. The debate has also continued in the scientific literature with a comment by McNeil “Evaluation of nanomedicines: stick to the basics” and a response by Chan.

Another comment by Lammers et al has been published 10 days ago “Cancer Nanomedicine: Is targeting our target?”. The implicit answer of the authors is no, targeting is not our target and therefore the absence of progress noted by Wilhelm et al matters little. Lammers et al’s argument is first that the percentage of the injected dose reaching the tumour is not a good indicator of the potential of a therapy, and second, that nanomedicine has in fact had some successes even without targeting. To illustrate this latter point, their first example is Doxil, a liposomal formulation of the anti-cancer drug Doxurubicin.

It is rather unconvincing that Lammers et al would use Doxil as an indication of the success of nanomedicine given that it was developed in the 80s and 90s, i.e. one or two decades before the “nanomedicine” word had been coined and Clinton had announced the $500M National Nanotechnology Initiative (January 2000). A bibliography search for the word “nanomedicine” suggests that it started to be used in the year 2000, with this MIT Technology Review being one of the very first examples:

Nanomedicine Nears the Clinic

Minuscule “smart bombs” that find cancer cells, kill them with the help of lasers and report the kills. Sound crazy? Guess again. That treatment scenario may be less than a decade away.

by David Voss
January 1, 2000

Since this infamous MIT technology review, we have seen so many similar promises and so little translation that Chan’s review and the debate that it provoked are indeed an incredibly positive and much needed development.

There is another amusing thing about Lammers et al’s review. The title suggesting that targeting is not our target is further echoed in the conclusion as follows:

“Patients do not benefit from targeting as such, and a reported tumour accumulation of 0.7%ID does not mean that nanomedicines do not work. We have to think beyond targeting, and beyond numbers, and focus on carrier-dependent drugs, combination therapies, protocols for patient selection and ways to enable rapid and more efficient clinical translation.”

Yet targeting as such seems very much to have been the target of these authors as the (non-exhaustive) list of articles below illustrate.

1. Blume, G.; Cevc, G.; Crommelin, M.; Bakkerwoudenberg, I.; Kluft, C.;Storm, G.,Specific targeting with poly(ethylene glycol)-modified liposomes – coupling of homing devices to the ends of the polymeric chains combines effective target binding with long circulation times. Biochimica Et Biophysica Acta 1993, 1149 (1), 180-184.
2. Vingerhoeds, M. H.; Steerenberg, P. A.; Hendriks, J.; Dekker, L. C.; vanHoesel, Q.;Crommelin, D. J. A.; Storm, G., Immunoliposome-mediated targeting of doxorubicin to human ovarian carcinoma in vitro and in vivo. British Journal of Cancer 1996, 74 (7), 1023-1029.
   3. Storm, G.; Crommelin, D. J. A., Colloidal systems for tumor targeting. Hybridoma 1997, 16 (1), 119-125.
   4. Mastrobattista, E.; Koning, G. A.; Storm, G., Immunoliposomes for the targeted delivery of antitumor drugs. Advanced Drug Delivery Reviews 1999, 40 (1-2), 103-127.
   5. Mastrobattista, E.; Kapel, R. H. G.; Eggenhuisen, M. H.; Roholl, P. J. M.; Crommelin, D. J. A.; Hennink, W. E.; Storm, G., Lipid-coated polyplexes for targeted gene delivery to ovarian carcinoma cells. Cancer Gene Therapy 2001, 8 (6), 405-413.
   6. Mastrobattista, E.; Crommelin, D. J. A.; Wilschut, J.; Storm, G., Targeted liposomes for delivery of protein-based drugs into the cytoplasm of tumor cells. Journal of Liposome Research 2002, 12 (1-2), 57-65.
   7. Metselaar, J. M.; Bruin, P.; de Boer, L. W. T.; de Vringer, T.; Snel, C.; Oussoren, C.; Wauben, M. H. M.; Crommelin, D. J. A.; Storm, G.; Hennink, W. E., A novel family of L-amino acid-based biodegradable polymer-lipid conjugates for the development of long-circulating liposomes with effective drug-targeting capacity. Bioconjugate Chemistry 2003, 14 (6), 1156-1164.
   8. Metselaar, J. M.; Wauben, M. H. M.; Wagenaar-Hilbers, J. P. A.; Boerman, O. C.; Storm, G., Complete remission of experimental arthritis by joint targeting of glucocorticoids with long-circulating liposomes. Arthritis and Rheumatism 2003, 48 (7), 2059-2066.
   9. Schiffelers, R. M.; Koning, G. A.; ten Hagen, T. L. M.; Fens, M.; Schraa, A. J.; Janssen, A.; Kok, R. J.; Molema, G.; Storm, G., Anti-tumor efficacy of tumor vasculature-targeted liposomal doxorubicin. Journal of Controlled Release 2003, 91 (1-2), 115-122.
   10. Schmidt, J.; Metselaar, J. M.; Wauben, M. H. M.; Toyka, K. V.; Storm, G.; Gold, R., Drug targeting by long-circulating liposomal glucocorticosteroids increases therapeutic efficacy in a model of multiple sclerosis. Brain 2003, 126, 1895-1904.
   11. van Steenis, J. H.; van Maarseveen, E. M.; Verbaan, F. J.; Verrijk, R.; Crommelin, D. J. A.; Storm, G.; Hennink, W. E., Preparation and characterization of folate-targeted pEG-coated pDMAEMA-based polyplexes. Journal of Controlled Release 2003, 87 (1-3), 167-176.
   12. Mulder, W. J. M.; Strijkers, G. J.; Griffioen, A. W.; van Bloois, L.; Molema, G.; Storm, G.; Koning, G. A.; Nicolay, K., A liposomal system for contrast-enhanced magnetic resonance imaging of molecular targets. Bioconjugate Chemistry 2004, 15 (4), 799-806.
   13. Schiffelers, R. M.; Ansari, A.; Xu, J.; Zhou, Q.; Tang, Q. Q.; Storm, G.; Molema, G.; Lu, P. Y.; Scaria, P. V.; Woodle, M. C., Cancer siRNA therapy by tumor selective delivery with ligand-targeted sterically stabilized nanoparticle. Nucleic Acids Research 2004, 32 (19).
   14. Verbaan, F. J.; Oussoren, C.; Snel, C. J.; Crommelin, D. J. A.; Hennink, W. E.; Storm, G., Steric stabilization of poly(2-(dimethylamino)ethyt methacrytate)-based polyplexes mediates prolonged circulation and tumor targeting in mice. Journal of Gene Medicine 2004, 6 (1), 64-75.
   15. Visser, C. C.; Stevanovic, S.; Voorwinden, L. H.; van Bloois, L.; Gaillard, P. J.; Danhof, M.; Crommelin, D. J. A.; de Boer, A. G., Targeting liposomes with protein drugs to the blood-brain barrier in vitro. European Journal of Pharmaceutical Sciences 2005, 25 (2-3), 299-305.
   16. Zhang, C. F.; Jugold, M.; Woenne, E. C.; Lammers, T.; Morgenstern, B.; Mueller, M. M.; Zentgraf, H.; Bock, M.; Eisenhut, M.; Semmler, W.; Kiessling, F., Specific targeting of tumor angiogenesis by RGD-conjugated ultrasmall superparamagnetic iron oxide particles using a clinical 1.5-T magnetic resonance scanner. Cancer Research 2007, 67 (4), 1555-1562.
   17. Dolman, M. E. M.; Fretz, M. M.; Segers, G. W.; Lacombe, M.; Prakash, J.; Storm, G.; Hennink, W. E.; Kok, R. J., Renal targeting of kinase inhibitors. International Journal of Pharmaceutics 2008, 364 (2), 249-257.
   18. Lammers, T.; Hennink, W. E.; Storm, G., Tumour-targeted nanomedicines: principles and practice. British Journal of Cancer 2008, 99 (3), 392-397.
   19. Lammers, T.; Subr, V.; Peschke, P.; Kuhnlein, P.; Hennink, W. E.; Ulbrich, K.; Kiessling, F.; Heilmann, M.; Debus, J.; Huber, P. E.; Storm, G., Image-guided and passively tumour-targeted polymeric nanomedicines for radiochemotherapy. British Journal of Cancer 2008, 99 (6), 900-910.
   20. Rijcken, C. J. F.; Schiffelers, R. M.; van Nostrum, C. F.; Hennink, W. E., Long circulating biodegradable polymeric micelles: Towards targeted drug delivery. Journal of Controlled Release 2008, 132 (3), E33-E35.
   21. Crommelin, D. J. A., Nanotechnological approaches for targeted drug delivery: hype or hope? New Biotechnology 2009, 25, S34-S34.
   22. Mulder, W. J. M.; Castermans, K.; van Beijnum, J. R.; Egbrink, M.; Chin, P. T. K.; Fayad, Z. A.; Lowik, C.; Kaijzel, E. L.; Que, I.; Storm, G.; Strijkers, G. J.; Griffioen, A. W.; Nicolay, K., Molecular imaging of tumor angiogenesis using alpha v beta 3-integrin targeted multimodal quantum dots. Angiogenesis 2009, 12 (1), 17-24.
   23. Talelli, M.; Rijcken, C. J. F.; Lammers, T.; Seevinck, P. R.; Storm, G.; van Nostrum, C. F.; Hennink, W. E., Superparamagnetic Iron Oxide Nanoparticles Encapsulated in Biodegradable Thermosensitive Polymeric Micelles: Toward a Targeted Nanomedicine Suitable for Image-Guided Drug Delivery. Langmuir 2009, 25 (4), 2060-2067.
   24. Dolman, M. E. M.; Harmsen, S.; Storm, G.; Hennink, W. E.; Kok, R. J., Drug targeting to the kidney: Advances in the active targeting of therapeutics to proximal tubular cells. Advanced Drug Delivery Reviews 2010, 62 (14), 1344-1357.
   25. Lammers, T.; Subr, V.; Ulbrich, K.; Hennink, W. E.; Storm, G.; Kiessling, F., Polymeric nanomedicines for image-guided drug delivery and tumor-targeted combination therapy. Nano Today 2010, 5 (3), 197-212.
   26. Lammers, T.; Subr, V.; Ulbrich, K.; Peschke, P.; Huber, P. E.; Hennink, W. E.; Storm, G.; Kiessling, F., Long-Circulating and Passively Tumor-Targeted Polymer-Drug Conjugates Improve the Efficacy and Reduce the Toxicity of Radiochemotherapy. Advanced Engineering Materials 2010, 12 (9), B413-B421.
   27. Oerlemans, C.; Bult, W.; Bos, M.; Storm, G.; Nijsen, J. F. W.; Hennink, W. E., Polymeric Micelles in Anticancer Therapy: Targeting, Imaging and Triggered Release. Pharmaceutical Research 2010, 27 (12), 2569-2589.
   28. Talelli, M.; Iman, M.; Rijcken, C. J. F.; van Nostrum, C. F.; Hennink, W. E., Targeted core-crosslinked polymeric micelles with controlled release of covalently entrapped doxorubicin. Journal of Controlled Release 2010, 148 (1), E121-E122.
   29. van Rooy, I.; Cakir-Tascioglu, S.; Couraud, P. O.; Romero, I. A.; Weksler, B.; Storm, G.; Hennink, W. E.; Schiffelers, R. M.; Mastrobattista, E., Identification of Peptide Ligands for Targeting to the Blood-Brain Barrier. Pharmaceutical Research 2010, 27 (4), 673-682.
   30. Talelli, M.; Hennink, W. E., Thermosensitive polymeric micelles for targeted drug delivery. Nanomedicine 2011, 6 (7), 1245-1255.
   31. Talelli, M.; Rijcken, C. J. F.; Oliveira, S.; van der Meel, R.; Henegouwen, P.; Lammers, T.; van Nostrum, C. F.; Storm, G.; Hennink, W. E., Nanobody – Shell functionalized thermosensitive core-crosslinked polymeric micelles for active drug targeting. Journal of Controlled Release 2011, 151 (2), 183-192.
   32. van Rooy, I.; Mastrobattista, E.; Storm, G.; Hennink, W. E.; Schiffelers, R. M., Comparison of five different targeting ligands to enhance accumulation of liposomes into the brain. Journal of Controlled Release 2011, 150 (1), 30-36.
   33. Crielaard, B. J.; Lammers, T.; Schiffelers, R. M.; Storm, G., Drug targeting systems for inflammatory disease: One for all, all for one. Journal of Controlled Release 2012, 161 (2), 225-234.
   34. Crielaard, B. J.; Rijcken, C. J. F.; Quan, L. D.; van der Wal, S.; Altintas, I.; van der Pot, M.; Kruijtzer, J. A. W.; Liskamp, R. M. J.; Schiffelers, R. M.; van Nostrum, C. F.; Hennink, W. E.; Wang, D.; Lammers, T.; Storm, G., Glucocorticoid-Loaded Core-Cross-Linked Polymeric Micelles with Tailorable Release Kinetics for Targeted Therapy of Rheumatoid Arthritis. Angewandte Chemie-International Edition 2012, 51 (29), 7254-7258.
   35. Dolman, M. E. M.; Harmsen, S.; Pieters, E. H. E.; Sparidans, R. W.; Lacombe, M.; Szokol, B.; Orfi, L.; Keri, G.; Storm, G.; Hennink, W. E.; Kok, R. J., Targeting of a platinum-bound sunitinib analog to renal proximal tubular cells. International Journal of Nanomedicine 2012, 7, 417-433.
   36. Joshi, M. D.; Unger, W. J.; Storm, G.; van Kooyk, Y.; Mastrobattista, E., Targeting tumor antigens to dendritic cells using particulate carriers. Journal of Controlled Release 2012, 161 (1), 25-37.
   37. Kunjachan, S.; Jayapaul, J.; Mertens, M. E.; Storm, G.; Kiessling, F.; Lammers, T., Theranostic Systems and Strategies for Monitoring Nanomedicine-Mediated Drug Targeting. Current Pharmaceutical Biotechnology 2012, 13 (4), 609-622.
   38. Lammers, T.; Kiessling, F.; Hennink, W. E.; Storm, G., Drug targeting to tumors: Principles, pitfalls and (pre-) clinical progress. Journal of Controlled Release 2012, 161 (2), 175-187.
   39. van der Meel, R.; Oliveira, S.; Altintas, I.; Haselberg, R.; van der Veeken, J.; Roovers, R. C.; Henegouwen, P.; Storm, G.; Hennink, W. E.; Schiffelers, R. M.; Kok, R. J., Tumor-targeted Nanobullets: Anti-EGFR nanobody-liposomes loaded with anti-IGF-1R kinase inhibitor for cancer treatment. Journal of Controlled Release 2012, 159 (2), 281-289.
   40. Talelli, M.; Oliveira, S.; Rijcken, C. J. F.; Pieters, E. H. E.; Etrych, T.; Ulbrich, K.; van Nostrum, R. C. F.; Storm, G.; Hennink, W. E.; Lammers, T., Intrinsically active nanobody-modified polymeric micelles for tumor-targeted combination therapy. Biomaterials 2013, 34 (4), 1255-1260.
   41. van der Meel, R.; Vehmeijer, L. J. C.; Kok, R. J.; Storm, G.; van Gaal, E. V. B., Ligand-targeted particulate nanomedicines undergoing clinical evaluation: Current status. Advanced Drug Delivery Reviews 2013, 65 (10), 1284-1298.
   42. Heukers, R.; Altintas, I.; Raghoenath, S.; De Zan, E.; Pepermans, R.; Roovers, R. C.; Haselberg, R.; Hennink, W. E.; Schiffelers, R. M.; Kok, R. J.; Henegouwen, P., Targeting hepatocyte growth factor receptor (Met) positive tumor cells using internalizing nanobody-decorated albumin nanoparticles. Biomaterials 2014, 35 (1), 601-610.
   43. Kunjachan, S.; Pola, R.; Gremse, F.; Theek, B.; Ehling, J.; Moeckel, D.; Hermanns-Sachweh, B.; Pechar, M.; Ulbrich, K.; Hennink, W. E.; Storm, G.; Lederle, W.; Kiessling, F.; Lammers, T., Passive versus Active Tumor Targeting Using RGD- and NGR-Modified Polymeric Nanomedicines. Nano Letters 2014, 14 (2), 972-981.
   44. Novo, L.; Mastrobattista, E.; van Nostrum, C. F.; Hennink, W. E., Targeted Decationized Polyplexes for Cell Specific Gene Delivery. Bioconjugate Chemistry 2014, 25 (4), 802-812.
   45. Theek, B.; Gremse, F.; Kunjachan, S.; Fokong, S.; Pola, R.; Pechar, M.; Deckers, R.; Storm, G.; Ehling, J.; Kiessling, F.; Lammers, T., Characterizing EPR-mediated passive drug targeting using contrast-enhanced functional ultrasound imaging. Journal of Controlled Release 2014, 182, 83-89.
   46. Liu, J.; Jiang, X. L.; Hennink, W. E.; Zhuo, R. X., A modular approach toward multifunctional supramolecular nanopolyplexes for targeting gene delivery. Journal of Controlled Release 2015, 213, E123-E124.
   47. Novo, L.; Takeda, K. M.; Petteta, T.; Dakwar, G. R.; van den Dikkenberg, J. B.; Remaut, K.; Braeckmans, K.; van Nostrum, C. F.; Mastrobattista, E.; Hennink, W. E., Targeted Decationized Polyplexes for siRNA Delivery. Molecular Pharmaceutics 2015, 12 (1), 150-161.
   48. Shi, Y.; Lammers, T.; van Nostrum, C.; Hennink, W. E., Long circulating and stable polymeric micelles for targeted delivery of paclitaxel. Journal of Controlled Release 2015, 213, E127-E128.
   49. Shi, Y.; van der Meel, R.; Theek, B.; Blenke, E. O.; Pieters, E. H. E.; Fens, M.; Ehling, J.; Schiffelers, R. M.; Storm, G.; van Nostrum, C. F.; Lammers, T.; Hennink, W. E., Complete Regression of Xenograft Tumors upon Targeted Delivery of Paclitaxel via Pi-Pi Stacking Stabilized Polymeric Micelles. Acs Nano 2015, 9 (4), 3740-3752.
   50. Ashton, S.; Song, Y. H.; Nolan, J.; Cadogan, E.; Murray, J.; Odedra, R.; Foster, J.; Hall, P. A.; Low, S.; Taylor, P.; Ellston, R.; Polanska, U. M.; Wilson, J.; Howes, C.; Smith, A.; Goodwin, R. J. A.; Swales, J. G.; Strittmatter, N.; Takats, Z.; Nilsson, A.; Andren, P.; Trueman, D.; Walker, M.; Reimer, C. L.; Troiano, G.; Parsons, D.; De Witt, D.; Ashford, M.; Hrkach, J.; Zale, S.; Jewsbury, P. J.; Barry, S. T., Aurora kinase inhibitor nanoparticles target tumors with favorable therapeutic index in vivo. Science Translational Medicine 2016, 8 (325).

 

A welcome Nature Editorial

I reproduce below a comment I have left on this Nature editorial entitled “Go forth and replicate!“.

Nature Publishing Group encouragement of replications and discussions of their own published studies is a very welcome move. Seven years ago, I wrote a letter (accompanying a submission) to the Editor of Nature Materials. The last paragraph of that letter read: “The possibility of refuting existing data and theories is an important condition of progress of scientific knowledge. The high-impact publication of wrong results can have a real impact on research activities and funding priorities. There is no doubt that the series of papers revisited in this Report contribute to shape the current scientific landscape in this area of science and that their refutation will have a large impact.” [1]

The submission was “Stripy Nanoparticles Revisited” and it took three more years to publish it… in another journal; meanwhile Nature Materials continued to publish findings based on the original flawed paper [2]. The ensuing, finally public (after three years in the secret of peer review), discussions on blogs, news commentary and follow up articles were certainly very informative on the absolute necessity of changing the ways we do science to ensure a more rapid discussion of research results [3].

One of the lessons I draw from this adventure is that the traditional publishing system is, at best ill suited (e.g. Small: three years delay), or at worst (e.g. Nature Materials) completely reluctant at considering replications or challenges to their published findings. Therefore, I am now using PrePrints (e.g. to publish a letter PNAS won’t share with their readers [4]), PubPeer and journals such as ScienceOpen where publication happens immediately and peer review follows [5].

So whilst I warmly welcome this editorial, it will need a little more to convince me that it is not a complete waste of time to use the traditional channels to open discussions of published results.

[1] The rest of letter can be found at https://raphazlab.wordpress.com/2012/12/17/letter-to-naturematerials/
[2] The article was eventually published in Small (DOI:10.1002/smll.201001465

2 comments on PubPeer

); timeline: https://raphazlab.wordpress.com/2012/12/20/stripy-timeline/
[3] https://raphazlab.wordpress.com/stripy-outside/
[4] https://raphazlab.wordpress.com/2015/11/16/pnas-your-letter-does-not-contribute-significantly-to-the-discussion-of-this-paper/
[5] https://raphazlab.wordpress.com/2015/11/17/the-spherical-nucleic-acids-mrna-detection-paradox/

Nanoparticles for imaging and sensing in biology

This is the title of a 3x1H45 course which I will give early September at the European School On Nanosciences and Nanotechnologies (ESONN) in Grenoble. The focus is on inorganic nanoparticles, e.g. gold, silver, iron oxide, quantum dots for biological applications. It will be the third year I give this course. It is a small class format with 21 students coming from all over the world, from New Zealand to South Africa, Denmark, Italy, India and France.

I have opted for a mostly discussion-based format centered around selected publications. I am asking readers of this blog (optional but very much welcome!) as well as students registered for the track B of ESONN15 (mandatory) to suggest at least one article for discussion. To suggest a paper, simply add a comment to this post with a reference (link to the paper would be even better).

Papers can be selected because they are historic landmarks in the field; or because they are recent ground breaking discoveries; or because they raise important questions that we need to discuss to move forward. Please provide one or two lines of justification for why you think we should discuss this paper.

Over to you!